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.

Detection of Lung Tumor Using ASPP-Unet with Whale Optimization Algorithm

The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult,automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

Industrial Internet of Things(IIoT)is an emerging field which connects digital equipment as well as services to physical systems.Intrusion detection systems(IDS)can be designed to protect the system from intrusions or attacks.In this view,this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection(HDL-MEID)technique for clustered IIoT environments.The HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure communication.Primarily,the HDL-MEID technique designs a new chaotic mayfly optimization(CMFO)based clustering approach for the effective choice of the Cluster Heads(CH)and organize clusters.Moreover,equilibrium optimizer with hybrid convolutional neural network long short-term memory(HCNNLSTM)based classification model is derived to identify the existence of the intrusions in the IIoT environment.Extensive experimental analysis is performed to highlight the enhanced outcomes of the HDL-MEID technique and the results were investigated under different aspects.The experimental results highlight the supremacy of the proposed HDL-MEID technique over recent state-of-the-art techniques.