Advanced Skin Lesion Classification Using Generative AI and Deep Learning Techniques

Skin cancer, an abnormal lesion growth, is one of the deadliest types of cancer. The accurate diagnosis and classification of skin lesions while distinguishing the malignant tumors from dermoscopic images are among the challenging tasks set by the professional dermatologists. This research seeks to employ advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) in combination with Generative Adversarial Networks (GANs), toward efficacious improvement of skin lesion classification and cancer detection. The dataset was processed using the ISIC 2024 benchmark and includes steps such as resizing, noise reduction, and data augmentation. GANs afforded synthetically generated training image data, effectively increasing both the augmentation in size and diversity of the training set as well as addressing the common challenge of class imbalance. Pre-Trained models conceived such as Resnet50 and Inceptionv3 (GoogLeNet) use transfer learning to extract hierarchical features from images and categorize skin lesions into malignant or benign classes. The results from these models were merged together to attain higher accuracy in the classification. Using the GANs significantly boosted these CNN models’ performance by enriching training data with synthetic but realistic samples, achieving a higher accuracy. This approach would avail an accurate and automated system for skin lesion classification, which in turn may assist in their early diagnosis regarding skin cancer by classifying them as malignant or benign, besides enhancing clinical decision-making with improved patient outcomes. Such applications of GANs for data augmentation emphasize their ability to address data limitations in medical imaging.