Optimized Convolutional Neural Network Models for Skin Lesion Classification
作者机构：Department of Electronics and AutomationUniversidad Autónoma deManizalesManizales170001Colombia SEDMATECCorporación Universitaria Autónoma de Nari駉Pasto520002Colombia Department of Computer ScienceFaculty of Computers and InformationSouth Valley UniversityQena83523Egypt Department of Computer ScienceUniversidad Autónoma de ManizalesManizales170001Colombia Department of Systems and InformaticsUniversidad de CaldasManizales170001Colombia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第70卷第2期
页 面：2131-2148页
核心收录：
学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1002[医学-临床医学] 0805[工学-材料科学与工程（可授工学、理学学位）] 100214[医学-肿瘤学] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This research is supported by the Universidad Autónoma de Manizales Manizales Colombia under project No.589-089.
主 题：Deep learning skin lesion convolutional neural network data augmentation transfer learning
摘 要：Skin cancer is one of themost severe diseases,andmedical imaging is among themain tools for cancer diagnosis.The images provide information on the evolutionary stage,size,and location of tumor lesions.This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks(CNNs)in distinguishing different skin lesions.The CNNs are based on transfer learning,taking advantage of ImageNet weights.Accordingly,in each experiment,different workflow stages are tested,including data augmentation and fine-tuning optimization.Three CNN models based on DenseNet-201,Inception-ResNet-V2,and Inception-V3 are proposed and compared using the HAM10000 dataset.The results obtained by the three models demonstrate accuracies of 98%,97%,and 96%,respectively.Finally,the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%.The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.
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