Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

作     者：Prasanalakshmi Balaji B.Sri Revathi Praveetha Gobinathan Shermin Shamsudheen Thavavel Vaiyapuri 

作者机构：Department of Computer ScienceKing Khalid UniversitySaudi Arabia School of Electrical EngineeringVellore Institute of TechnologyIndia Department of Computer ScienceJazan UniversitySaudi Arabia College of Computer Engineering and SciencePrince Sattam bin Abdulaziz UniversitySaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第11期

页      面：2275-2291页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research is financially supported by the Deanship of Scientific Research at King Khalid University under research grant number(RGP.2/202/43) 

主　　题：Deep belief neural network mayfly optimization gaussian filter contrast normalization grey level variance local binary pattern discrete wavelet transform 

摘      要：Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical *** Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare *** recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image *** medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial *** newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death *** on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this *** context,primarily image quality enhancement procedures like noise removal and contrast normalization are *** the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are *** extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant *** selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar *** proposed model is evaluated in terms of accuracy,precision,recall,and f-