GC-123 Deep Learning-Based Skin Cancer Detection

Skin cancer is increasingly becoming a severe health problem globally today, but early detection is essential to enhance survival rates. Nonetheless, conventional diagnosis relies largely on visual examinations by dermatologists, which can be subjective and time-consuming. This research examines the application of deep learning for the automation of skin cancer detection based on dermoscopic images from the HAM10000 dataset. The models VGG19, DenseNet121 and ResNet152 will be trained and evaluated, with class mbalance addressed using data augmentation strategies. The outputs will demonstrate the applicability of deep learning to improve skin cancer diagnosis. Classification optimization using ensemble modeling and its improved architecture with an attention U-Net to offer segmentation integration for improved lesion localization and explainability will be future research.