Published Paper Details:

Skin cancer refers to the abnormal growth of skin cells, typically triggered by DNA damage from exposure to ultraviolet (UV) radiation, leading to uncontrolled cell division and the formation of malignant tumors. Common signs of skin cancer include changes in the size, shape, color, or texture of moles or lesions, as well as the development of new growths or sores that do not heal. Early detection of skin cancer is crucial for successful treatment and improved prognosis. However, traditional methods of skin cancer diagnosis are often subjective, time-consuming, and reliant on expert interpretation, leading to challenges in accessibility and accuracy. In recent years, advances in image processing and machine learning have offered promising avenues for improving skin cancer detection through automated analysis of dermatoscopic images. It proposed the development and implementation of a novel skin cancer detection system utilizing convolutional neural networks (CNNs) for automated lesion classification. The system integrates features for data preprocessing, feature extraction, CNN model training, and diagnostic output visualization. Leveraging a diverse dataset of dermatoscopic images, the CNN model demonstrates high accuracy and robust performance in distinguishing between benign and malignant lesions.