Employing Convolutional Neural Networks for the Early Identification of Skin Cancer

Abstract

Preventing skin cancer, particularly melanoma, is crucial for reducing mortality rates and improving patient prognosis. This study proposes a comprehensive approach that utilises convolutional neural networks (CNNs) to further improve the accuracy and diagnosis of skin cancer.   Our methodology involves developing and improving a sophisticated deep learning model by utilising an extensive collection of dermatological images that cover a wide range of skin types and stages of malignancy. The CNN model effectively distinguishes between benign and malignant tumors due to its capacity to collect and comprehend intricate information from these pictures. We emphasise the power of early treatment of cancer and strive to reduce the occurrence of false negatives, a common issue associated with traditional diagnostic methods.  Our approach streamlines the integration of this state-of-the-art technology into clinical practice by offering dermatologists a user-friendly interface.  An extensive assessment of the system's effectiveness, based on current diagnostic criteria, shown a significant improvement in the rates of early detection.   This work not only demonstrates the potential of CNNs in medical imaging, but also paves the way for their application in cancer and dermatology, ultimately leading to enhanced patient outcomes