Dermatological Decision Support Systems using CNN for Binary Classification
Received: 5 March 2024 | Revised: 26 March 2024 | Accepted: 28 March 2024 | Online: 13 April 2024
Corresponding author: Rajendra Dev Dondapati
Abstract
Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy.
Keywords:
ISIC 2017, VGG16, ResNet50, InceptionV3Downloads
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