Classification of Skin Lesions using PyQt5 and Deep Learning Methods

Dina Koishiyeva, Alibek Bissembayev, Teodor B Iliev, Jeong Won Kang, Assel Mukasheva

2024

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DOI 10.1109/ciees62939.2024.10811274
Data confidence 70 medium
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Abstract

This study presents the OncoScan system designed to assist dermatologists in classifying skin diseases using an improved DenseNet121 model. The system was tested on the large HAM10000 dataset of over 10,000 images of skin lesions in seven categories. The DenseNet121 model, characterized by dense connectivity and transient layers, was compared with the InceptionV3 and EfficientNetB7 models. The results demonstrated the superiority of the DenseNet121 model, with an accuracy of 86.04% and an AUC of 96.32%. The OncoScan interface, developed using PyQt5, supports multilingual functionality including English, Russian, and Kazakh, and offers image processing tools. The system's ability to handle image processing and classification with ease makes it a valuable asset for dermatologic diagnostics.