A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication standards.The Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and integrity.The data need a space away fromunauthorized access,modification,or deletion.These requirements are to be fulfilled by digital image watermarking and encryption.This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard.It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver side.The proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD watermarking.In addition,image encryption is implemented based on chaos and DNA encoding.The encrypted watermarked images are then transmitted through the wireless SC-FDMA system.The linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images.Two subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this paper.The study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images.In addition,different variants of SC-FDMA based on the Discrete Wavelet Transform(DWT),Discrete Cosine Transform(DCT),and Fast Fourier Transform(FFT)are considered and compared for the image communication task.The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels,while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels.

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy.Deep learning provides a high performance for several medical image analysis applications.This paper proposes a deep learning model for the medical image fusion process.This model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR images.Then,an additional process is executed on the extracted features.After that,the fused feature map is reconstructed to obtain the resulting fused image.Finally,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality.Different realistic datasets of different modalities and diseases are tested and implemented.Also,real datasets are tested in the simulation analysis.

COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its aftermath.Due to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big quantity.Patients who test positive for Covid-19 are diagnosed via a nasal PCR test.In comparison,polymerase chain reaction(PCR)findings take a few hours to a few days.The PCR test is expensive,although the government may bear expenses in certain places.Furthermore,subsets of the population resist invasive testing like swabs.Therefore,chest X-rays or Computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response time.Recent advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest x-rays.This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme.In order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the server.These two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the globalmodel.The proposed model is trained using an image dataset that can be found on Kaggle.There are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are used.Each edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has 5800.There is no association between the datasets of the various nodes that are included in the network.By doing it in this manner,each of the nodes will have access to a separate image collection that has no correlation with each other.The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset,and the findings that we have obtained are quite encouraging.

Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the future.Innovative techniques have been suggested for detecting attacks using machine learning and deep learning.The significant advantage of deep learning is that it is highly efficient,but it needs a large training time with a lot of data.Therefore,in this paper,we present a new feature reduction strategy based on Distributed Cumulative Histograms(DCH)to distinguish between dataset features to locate the most effective features.Cumulative histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification results.Three different models for detecting attacks using Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)are also proposed.The accuracy test of attack detection using the hybrid model was 98.96%on the UNSW-NP15 dataset.The proposed model is compared with wrapper-based and filter-based Feature Selection(FS)models.The proposed model reduced classification time and increased detection accuracy.

Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

Next-generation networks,including the Internet of Things(IoT),fifth-generation cellular systems(5G),and sixth-generation cellular systems(6G),suf-fer from the dramatic increase of the number of deployed devices.This puts high constraints and challenges on the design of such networks.Structural changing of the network is one of such challenges that affect the network performance,includ-ing the required quality of service(QoS).The fractal dimension(FD)is consid-ered one of the main indicators used to represent the structure of the communication network.To this end,this work analyzes the FD of the network and its use for telecommunication networks investigation and planning.The clus-ter growing method for assessing the FD is introduced and analyzed.The article proposes a novel method for estimating the FD of a communication network,based on assessing the network’s connectivity,by searching for the shortest routes.Unlike the cluster growing method,the proposed method does not require multiple iterations,which reduces the number of calculations,and increases the stability of the results obtained.Thus,the proposed method requires less compu-tational cost than the cluster growing method and achieves higher stability.The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks.The developed method is evaluated for two different network structures and compared with the cluster growing method.Results validate the developed method.

Dynamic S-Box Generation Using Novel Chaotic Map with Nonlinearity Tweaking

A substitution box(S-Box)is a crucial component of contemporary cryptosystems that provide data protection in block ciphers.At the moment,chaotic maps are being created and extensively used to generate these SBoxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical attempts.In this paper,the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking approach.The projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their workingmakes both of these dynamic in nature.The tweak approach employs cubic polynomials while permuting the values of an initial S-Box to enhance its cryptographic fort.Values of the parameters are provided using the cipher key and a small variation in values of these parameters results in a completely different unique S-Box.Comparative analysis and exploration confirmed that the projected chaoticmap exhibits a significant amount of chaotic complexity.The security assessment in terms of bijectivity,nonlinearity,bits independence,strict avalanche,linear approximation probability,and differential probability criteria are utilized to critically investigate the effectiveness of the proposed S-Box against several assaults.The proposed S-Box’s cryptographic performance is comparable to those of recently projected S-Boxes for its adaption in real-world security applications.The comparative scrutiny pacifies the genuine potential of the proposed S-Box in terms of its applicability for data security.

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

Biometric security is a growing trend,as it supports the authentication of persons using confidential biometric data.Most of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these sys-tems.Biometric systems provide sufficient protection and privacy for users.The recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of attacks.In addition,they have not been studied on different and large biometric datasets.Another point that deserves consideration is the hardware implementation of cancellable biometric recognition systems.This paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic cryptosystem.The rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of users.The suggested cryptosystem adds significant permutation and diffusion to the encrypted biometric templates.We introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against attacks.In addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is introduced.The obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition system.The obtained results prove that the proposed cryptosystem enhances the security and leads to better efficiency of the cancellable biometric recognition system in the presence of different types of attacks.

Optical Ciphering Scheme for Cancellable Speaker Identification System

Most current security and authentication systems are based on personal biometrics.The security problem is a major issue in the field of biometric systems.This is due to the use in databases of the original biometrics.Then biometrics will forever be lost if these databases are attacked.Protecting privacy is the most important goal of cancelable biometrics.In order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases.One methodology to achieve non-invertibility is the employment of non-invertible transforms.This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system.This system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption process.The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and feasible.The encryption and cancelability effects are good and reveal good performance.Also,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Digital Object Architecture for IoT Networks

The Internet of Things(IoT)is a recent technology,which implies the union of objects,“things”,into a single worldwide network.This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices.Device identification is one of these challenges that becomes complicated with the increase of network devices.Despite this,there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications.In this regard,one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks.Many unique soft-ware and hardware solutions are used as a unique global identifier;however,such solutions have many limitations.This article proposes a novel solution,based on the Digital Object Architecture(DOA),that meets the requirements of identifying devices and applications of the IoT.This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks.We propose a model of an identification system based on the architecture of digital objects,which differs from the well-known ones.The proposed model ensures an acceptable quality of service(QoS)in the common architecture of the existing public communication networks.A novel interaction architecture is developed by introducing a Middle Handle Register(MHR)between the global register,i.e.,Global Handle Register(GHR),and local register,i.e.,Local Handle Register(LHR).The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented.The developed model is simulated for a wide-area network with allocated registers,and the results are introduced and discussed.

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension.The classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral features.For this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral bands.The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel.The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system.In this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in conjunction.The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI.In the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information.The resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost.The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF.The classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa factor.The proposed scheme achieved greater than 90%results for all the performance evaluation metrics.The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range.The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.

Proposed Privacy Preservation Technique for Color Medical Images

Nowadays,the security of images or information is very important.This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security.First,the secret medical image is encrypted using Advanced Encryption Standard(AES)algorithm.Then,the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit(LSB)watermarking algorithm.After that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)algorithm.Afterwards,the stego-image obtained is split into 16 parts.Finally,it is sent to the receiver.We adopt this strategy to send the secret medical image and report over a network securely.The proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to confirm the matching between the secret medical image before and after transmission.Simulation results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.

An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

COVID-19 remains to proliferate precipitously in the world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection process.For the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis system.Once the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and efforts of specialists with the explosion of COVID-19 pandemic in the world.The proposed classification approach is applied for different scenarios of 80%,70%,or 60%of the data for training and 20%,30,or 40%of the data for testing,respectively.In these scenarios,the proposed approach achieves classification accuracies of 100%,99.45%,and 98.55%,respectively.Thus,the obtained results demonstrate and prove the efficacy of the proposed approach for assisting the specialists in automated medical diagnosis services.

An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images.The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

Efficient Forgery Detection Approaches for Digital Color Images

This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data communicated over networks. Hence, there is an urgent need fordeveloping efficient image forgery detection algorithms. Two main types offorgery are considered in this paper: splicing and copy-move. Splicing isperformed by inserting a part of an image into another image. On the otherhand, copy-move forgery is performed by copying a part of the image intoanother position in the same image. The proposed approach for splicingdetection is based on the assumption that illumination between the originaland tampered images is different. To detect the difference between the originaland tampered images, the homomorphic transform separates the illuminationcomponent from the reflectance component. The illumination histogramderivative is used for detecting the difference in illumination, and henceforgery detection is accomplished. Prior to performing the forgery detectionprocess, some pre-processing techniques, including histogram equalization,histogram matching, high-pass filtering, homomorphic enhancement, andsingle image super-resolution, are introduced to reinforce the details andchanges between the original and embedded sections. The proposed approachfor copy-move forgery detection is performed with the Speeded Up RobustFeatures (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matchingwith Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogramequalization and single-mage super-resolution. Simulation results proved thefeasibility and the robustness of the pre-processing step in homomorphicdetection and SURF detection algorithms for splicing and copy-move forgerydetection, respectively.

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent years.Early diagnosis is essential to handle breast cancer patients for treatment at the right time.Screening with mammography is the preferred examination for breast cancer,as it is available worldwide and inexpensive.Computer-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,early.The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations.Processing of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal classes.This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses.The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement technique.The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast metrics.The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives.The segmentation process is performed using Otsu’smultiple thresholding method.This method segments the image regions into five classes with variable intensities using four thresholds.Its effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of masses.The performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)metrics.The segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an expert.Hence,the proposed framework achieves promising results for aiding radiologists in screening of mammograms,accurately.