(Department of Informatics, Universitas Pembangunan Jaya, South Tangerang, 15413, Indonesia)
(2) Ida Nurhaida
(Center for Urban Studies, Universitas Pembangunan Jaya, South Tangerang, 15413, Indonesia)*Corresponding author
AbstractSkin cancer continues to show a significant global increase in incidence, and early detection remains essential to reducing mortality rates. Conventional diagnostic techniques such as biopsy are invasive, require considerable processing time, and are not always accessible, particularly in remote or resource-limited healthcare environments, indicating the need for an intelligent and efficient diagnostic support system. This study develops a lightweight Convolutional Neural Network (CNN) model designed to classify seven types of skin lesions using the HAM10000 dataset consisting of 10,015 dermatoscopic images. The preprocessing pipeline involved resizing, normalization, oversampling, and dataset splitting. The training process was conducted for a maximum of 40 epochs and concluded automatically at epoch 29 using early stopping to prevent overfitting. The experimental results demonstrated that the proposed model achieved an accuracy of 98%, and surpassed common pretrained architectures including ResNet50V2 (83%) and VGG19 (67%), with precision, recall, and F1-score metrics showing consistent performance across all lesion classes. The final trained model was integrated into the Dermascan web platform, enabling real-time automated lesion classification from user-uploaded images. These findings confirm that the lightweight CNN model offers a reliable, fast, and accessible tool for early skin cancer detection that can be beneficial for both clinical decision-support and wider public healthcare applications.
KeywordsConvolutional Neural Network; Dermatoscopic Images; HAM10000; Skin Cancer Classification; Web-Based Detection System
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DOIhttps://doi.org/10.33122/ejeset.v6i2.1243 |
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