Early Detection of Malignant and Benign using Adaptive Deep Learning

Malignant is the most dangerous type of skin cancer, and it is among the most common varieties worldwide. In order to improve patient outcomes and reduce mortality rates, early detection is of vital importance. In recent years, deep learning models have demonstrated promising results on a variety of imaging tasks, including skin lesion classification. In this paper, we propose a method for the early detection of malignant and benign skin lesions using the ResN et50 convolutional neural network architecture. After training on a large dataset of labelled pictures of skin lesions, the proposed model achieves high accuracy in distinguishing benign from malignant lesions. Additionally, the implementation of a GUI interface enhances user accessibility and ease of use. The results highlight the effectiveness of the ResN et50 model in facilitating early detection, while the GUI interface augments its potential applicability in clinical settings.