Developing an Artificial Intelligence Model to Analyze Skin Images and Detect Skin Cancer in Its Early Stages

Abstract

Skin cancer, mainly melanoma, is one of the most competitive styles of most cancers, and early detection is vital for improving patient outcomes. This have a look at aimed to develop a convolutional neural network (CNN)-based model for the early detection of pores and skin most cancers the usage of dermatoscopic snap shots. The version was trained on a dataset from the International Skin Imaging Collaboration (ISIC) archive, which contained labeled pics of both benign and malignant pores and skin lesions. Transfer getting to know strategies, consisting of the usage of pre-trained fashions including ResNet50 and VGG16, had been employed to enhance the model's capacity to generalize. The version achieved robust performance throughout key assessment metrics, including an accuracy of 96.40% at the education set and 93.85% at the validation set. On an unseen check set, the version established an accuracy of 92.30%, with a precision of 89.80% and a don't forget of ninety.50%. The excessive region below the curve (AUC) score of 0.962 on the validation set and 0.948 at the check set confirms the model’s robust discriminatory strength in distinguishing between benign and malignant lesions. The tool was deployed as a person-pleasant internet-based totally application, allowing clinicians and patients to upload dermatoscopic snap shots for immediate prognosis, with effects integrated into scientific workflows via electronic fitness statistics (EHR). Despite its promising overall performance, the version's reliance on tremendous photos and the constrained diversity of the dataset highlights the want for further validation in actual-global clinical settings and throughout various populations. Future paintings will consciousness on enhancing the version’s robustness to photograph first-rate versions and expanding its applicability to a broader variety of skin kinds.