Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

A Method of Generating Semi-Experimental Biomedical Datasets

This paper proposed a method to generate semi-experimental biomedical datasets based on full-wave simulation software.The system noise such as antenna port couplings is fully considered in the proposed datasets,which is more realistic than synthetical datasets.In this paper,datasets containing different shapes are constructed based on the relative permittivities of human tissues.Then,a back-propagation scheme is used to obtain the rough reconstructions,which will be fed into a U-net convolutional neural network(CNN)to recover the high-resolution images.Numerical results show that the network trained on the datasets generated by the proposed method can obtain satisfying reconstruction results and is promising to be applied in real-time biomedical imaging.

Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung cancer.The recently developed deep learning(DL)models can be employed for the effectual identification and classification of diseases.This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT technique.The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images.The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature extraction.Also,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT images.Moreover,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch count.For demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis.Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously.Traditionally,physicians examine the characteristics of tongue prior to decision-making.In this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed.This model can reduce the physical harm made to the patients.Several tongue image analytical methodologies have been proposed earlier.However,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis model.With this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model.The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal process.Besides,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)algorithm.At last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color images.The design of OGSO algorithm for SqueezeNet model shows the novelty of the work.To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures.The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

Biomedical Image Processing Using FCM Algorithm Based on the Wavelet Transform

An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues.Biomedical image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,etc.At the same time,cervical cancer becomes a major reason for increased women’s mortality rate.But cervical cancer is an identified at an earlier stage using regular pap smear images.In this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)environment.The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition.In addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)technique.Moreover,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear images.Furthermore,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images.In order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev database.The experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

Intelligent Deep Transfer Learning Based Malaria Parasite Detection andClassification Model Using Biomedical Image

Malaria is a severe disease caused by Plasmodium parasites,which can be detected through blood smear images.The early identification of the disease can effectively reduce the severity rate.Deep learning(DL)models can be widely employed to analyze biomedical images,thereby minimizing the misclassification rate.With this objective,this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification(IDTL-MPDC)model on blood smear images.The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images.In addition,the IDTL-MPDC technique derives median filtering(MF)as a pre-processing step.In addition,a residual neural network(Res2Net)model was employed for the extraction of feature vectors,and its hyperparameters were optimally adjusted using the differential evolution(DE)algorithm.The k-nearest neighbor(KNN)classifier was used to assign appropriate classes to the blood smear images.The optimal selection of Res2Net hyperparameters by the DE model helps achieve enhanced classification outcomes.A wide range of simulation analyses of the IDTL-MPDC technique are carried out using a benchmark dataset,and its performance seems to be highly accurate(95.86%),highly sensitive(95.82%),highly specific(95.98%),with a high F1 score(95.69%),and high precision(95.86%),and it has been proven to be better than the other existing methods.

Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical images.In this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)model.The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma.At the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from images.In addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors.Then,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of osteosarcoma.EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results.The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study.In order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

A Robust Non-Blind Watermarking for Biomedical Images Based on Chaos

The advent of the Internet in these last years encouraged a considerable traffic of digital images. In the sanitary field, precisely in telemedicine branch, medical images play a very important role for therapeutic diagnoses. Thus, it is necessary to protect medical images data before transmission over the network to preserve their security and prevent unauthorized access. In this paper, a secure algorithm for biomedical images encryption scheme based on the combination of watermarking technique and chaotic function is proposed. In the proposed method, to achieve high security level performances, a non-blind hybrid watermarking technique with audio signal, Discrete Wavelet Transform is used;smoothness is also used as selected criteria;the iterations obtained by the chaotic sequences are essential and allow a good realization of the encryption process. One of the main advantages of chaos-based encryption schemes is the generation of a large number of key spaces to resist brute force attacks from the encryption algorithm. The experimental results presented in this paper attest to the invisibility and robustness of the proposed algorithm combining watermarking and chaos-based encryption.

Rising role of artificial intelligence in image reconstruction for biomedical imaging

In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning,AI technology plays an increasingly important role in biomedical image reconstruction.The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field.Favoring AI,the performance of biomedical image reconstruction has been improved in terms of accuracy,resolution,imaging speed,etc.We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction,and propose possible future directions in this field.

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.Due to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artificial Intelligence(AI)have become essential.In this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classification on Apache Spark environment.The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately.BDA-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classification.In addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image.Moreover,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor.Stochastic Gradient Descent(SGD)model is used for parameter tuning process.Furthermore,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classification model to determine the appropriate class labels to it.Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

Biomedical microwave-induced thermoacoustic imaging

Microwave induced thermoacoustic imaging(MTAI)has emerged as a potential biomedical imaging modality with over 20-year growth.MTAI typically employs pulsed microwave as the pumping source,and detects the microwave-induced ultrasound wave via acoustic transducers.Therefore,it features high acoustic resolution,rich elect romagnetic contrast,and large imaging depth.Benefiting from these unique advantages,MTAI has been extensively applied to various fields including pathology,biology,material and medicine.Till now,MTAI has been deployed for a wide range of biomedical applications,including cancer diagnosis,joint evaluation,brain in-vestigation and endoscopy.This paper provides a comprehensive review on(1)essential physics(endogenous/exogenous contrast mechanisms,penetration depth and resolution),(2)hardware configurations and software implementations(excit ation source,antenna,ultrasound detector and image recovery algorithm),(3)animal studies and clinical applications,and(4)future directions.

Biomedical X-ray Imaging Enabled by Carbon Nanotube X-ray Sources

Although discovered more than 100 years ago, X-ray source technology has evolved rather slowly. The recent invention of the carbon nanotube （CNT） X-ray source technology holds great promise to revolutionize the field of biomedical X-ray imaging. CNT X-ray sources have been successfully adapted to several biomedical imaging applications including dynamic rnicro-CT of small animals and stationary breast tomosynthesis of breast cancers. Yet their more irnportant biomedical imaging applications still lie ahead in the future, with the devel- oprnent of stationary rnulti-source CT as a noteworthy exarnple.

Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important regions.The typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or accuracy.Automatic BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical approaches.Therefore,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)model.The presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification process.To attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract features.Followed by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet method.At last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is utilized.The experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct factors.The experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

Automated cone photoreceptor cell identication in confocal adaptive optics scanning laser ophthalmoscope images based on object detection

Cone photoreceptor cell identication is important for the early diagnosis of retinopathy.In this study,an object detection algorithm is used for cone cell identication in confocal adaptive optics scanning laser ophthalmoscope(AOSLO)images.An effectiveness evaluation of identication using the proposed method reveals precision,recall,and F_(1)-score of 95.8%,96.5%,and 96.1%,respectively,considering manual identication as the ground truth.Various object detection and identication results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method.Overall,the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images,being comparable to manual identication.

Multimodal compression applied to biomedical data

In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel bio-signals (e.g. ECG, EEG). This technique is based on the idea of Multimodal Compression (MC) which requires only one codec instead of multiple codecs. Objectively, biosignal samples are merged in the spatial domain of the image using a specific mixing function. Afterwards, the whole mixture is compressed using JPEG 2000. The spatial mixing function inserts samples in low-frequency regions, defined using a set of operations, including down-sampling, interpolation, and quad-tree decomposition. The decoding is achieved by inverting the process using a separation function. Results show that this technique allows better performances in terms of Compression Ratio (CR) compared to approaches which encode separately modalities. The reconstruction quality is evaluated on a set of test data using the PSNR (Peak Signal Noise Ratio) and the PRD (Percent Root Mean Square Difference), respectively for the image and biosignals.

Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based death.Conventionally,pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples,stained and fixed through Haematoxylin and Eosin(H&E).The advancement of graphical processing systems has resulted in high potentiality for deep learning(DL)techniques in interpretating visual anatomy from high resolution medical images.This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)algorithm.The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal cancer.To accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image quality.In addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed images.Moreover,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal cancer.The utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach.A wide range of experiments was implemented on benchmark datasets to assess the classification performance.A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.

Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

Transfer Learning-Based Class Imbalance-Aware Shoulder Implant Classification from X-Ray Images

Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the joint.It is often challenging for doctors to identify the exact model and manufacturer of the prosthesis when it is unknown.This paper proposes a transfer learning-based class imbalance-aware prosthesis detection method to detect the implant’s manufacturer automatically from shoulder X-ray images.The framework of the method proposes a novel training approach and a new set of batch-normalization,dropout,and fully convolutional layers in the head network.It employs cyclical learning rates and weighting-based loss calculation mechanism.These modifications aid in faster convergence,avoid local-minima stagnation,and remove the training bias caused by imbalanced dataset.The proposed method is evaluated using seven well-known pre-trained models of VGGNet,ResNet,and DenseNet families.Experimentation is performed on a shoulder implant benchmark dataset consisting of 597 shoulder X-ray images.The proposed method improves the classification performance of all pre-trained models by 10–12%.The DenseNet-201-based variant has achieved the highest classification accuracy of 89.5%,which is 10%higher than existing methods.Further,to validate and generalize the proposed method,the existing baseline dataset is supplemented to six classes,including samples of two more implant manufacturers.Experimental results have shown average accuracy of 86.7%for the extended dataset and show the preeminence of the proposed method.