Computer Decision Support System for Skin Cancer Localization and Classification  

作　　者：Muhammad Attique Khan Tallha Akram Muhammad Sharif Seifedine Kadry Yunyoung Nam 

机构地区：[1]Department of Computer Science,COMSATS University Islamabad,Wah Campus,47040,Pakistan [2]Department of Computer and Electrical Engineering,COMSATS University Islamabad,Wah Campus,47040,Pakistan [3]Department of Mathematics,Beirut Arab University,Beirut,Lebanon [4]Department of Computer Science and Engineering,Soonchunhyang University,Asan,Korea

出　　处：《Computers, Materials & Continua》2021年第7期1041-1064,共24页计算机、材料和连续体（英文）

基　　金：supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist);the Soonchunhyang University Research Fund.

摘　　要：In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.

关 键 词：Skin cancer convolutional neural network lesion localization transfer learning features fusion features optimization 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]