Enhanced Diagnostic Precision:Deep Learning for Tumors Lesion Classification in Dermatology  

作　　者：Rafid Sagban Haydar Abdulameer Marhoon Saadaldeen Rashid Ahmed 

机构地区：[1]Engineering Technical College,Al-Ayen University,Thi-Qar,64001,Iraq [2]Information Technology College,University of Babylon,Hilla,51001,Iraq [3]Information and Communication Technology Research Group,Scientific Research Center,Al-Ayen University,Thi-Qar,64001,Iraq [4]College of Computer Sciences and Information Technology,University of Kerbala,Karbala,56001,Iraq [5]Artificial Intelligence Engineering Department,College of Engineering,Al-Ayen University,Thi-Qar,64001,Iraq [6]Computer Science,Bayan University,Erbil,44001,Iraq

出　　处：《Intelligent Automation & Soft Computing》2024年第6期1035-1051,共17页智能自动化与软计算（英文）

摘　　要：Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized cutaneous malignancies.Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma.The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma.The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions.The present study investigates the feasibility of automated methods for detecting pigmented skin lesions with potential malignancy.To diagnose skin lesions,medical professionals employ a two-step approach.Before detecting malignant types with other deep learning(DL)models,a preliminary step involves using a DL model to identify the skin lesions as either pigmented or non-pigmented.The performance assessments accurately assessed four distinct DL models:Long short-term memory(LSTM),Visual Geometry Group(VGG19),Residual Blocks(ResNet50),and AlexNet.The LSTM model exhibited higher classification accuracy compared to the other models used.The accuracy of LSTM for pigmented and non-pigmented,pigmented tumours and benign classes,and melanomas and pigmented nevus classes was 0.9491,0.9531,and 0.949,respectively.Automated computerized skin cancer detection promises to enhance diagnostic efficiency and precision significantly.

关 键 词：Pigmented lesions deep learning models skin cancer automated diagnosis basal cell carcinoma 

分 类 号：R739.5[医药卫生—肿瘤] TP39[医药卫生—临床医学]