Precision Diagnosis of Melanoma using Deep Learning Techniques on DICOM Dermoscopic Images

One of the most aggressive types of skin cancer is melanoma, and increasing patient survival rates requires early detection. In this study, we introduce a precision diagnostic system that analyzes DICOM dermoscopic images using cutting-edge deep learning models. On a sizable dataset of dermoscopic pictures, we trained two convolutional neural networks, ResNet and EfficientNet. EfficientNet outperformed ResNet in differentiating between benign and malignant lesions. A secondary classifier was used to further classify the lesions as either nevus or unknown in cases that were deemed benign, offering a deeper level of diagnostic information. In order to improve usability, we used Streamlit to create an interactive interface that lets clinicians view real-time diagnosis results and access other data that was taken from the DICOM metadata, such as patient demographics and picture capture information. A comprehensive solution for helping physicians make more informed and precise melanoma diagnosis is provided by this combination of sophisticated image analysis and metadata integration. The suggested system is intended to increase early detection, decrease human error, and improve diagnostic precision—all of which will lead to more successful melanoma treatment plans.