Analysis of GAN for Melanoma Skin CancerClassification with Dermatologist Recommendation

Melanoma is a type of skin cancer that poses a significant health threat globally, necessitating effective early detection methods. Melanoma arises from the pigment-producing cells(melanocytes) and is known for its higher metastatic potential. Designing a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection is challenging due to the appearance of the nevus. This study investigates the application of a GAN for classification of melanoma. The GAN architecture is employed to learn and generate synthetic melanoma images, enhancing the diversity and richness of the training dataset. We mainly focus on evaluating performance of GAN-enhanced model through comprehensive metrics like specificity, accuracy, the receiver operating characteristic curve (ROC) and area under the curve (AUC). This study sheds light on utility of synthetic data in clinical image analysis and provides insights into the strengths and limitations of utilizing GANs for melanoma skin cancer classification. We also added a dermatologist recommendation feature to our model. It uses computer programs to understand what youneed and then matches you with the best doctor available nearby. This system helps you get the care you need faster and easier, making sure you see a doctor who's a good fit for you and your health needs. This system matches patients with suitable doctors. This matching involves considering factors like doctor specialty and location.