AI-Powered Detection of Malignant Melanoma

Since malignant melanoma is a fatal skin cancer, survival depends on early detection. Even though standard diagnostic procedures require a high level of dermatological expertise, these Protocols can be lengthy and prone to mistakes. The purpose of this paper is to present a novel approach to AI.This is to use convolutional neural networks (CNNs) to au- tomatically detect malignant melanoma. Our method accurately distinguishes between benign and malignant skin lesions thanks to a cutting-edge deep learning model that was trained on a sizable dataset of dermatoscopic images. To greatly improve model performance, the new CNN architecture uses advanced techniques like transfer learning, extensive data augmentation, and cautious fine-tuning. According to our experiment results, our system performs traditional machine learning techniques in large number in terms of accuracy, sensitivity and specificity. Our approach is to use a reliable tool that has the potential for decision support. Dermatologists can use it.In several melanoma cases, it can also help with a much faster and more precise diagnosis. The study highlights how AI has the potential to revolutionize dermatology. It also identifies the most promising directions for artificial intelligence research.