HARNESSING DEEP NEURAL NETWORKS FOR EARLY DETECTION AND DIAGNOSIS OF MELANOMA

Abstract

ABSTRACT
Melanoma, one of the most aggressive forms of skin cancer, requires early detection for effective treatment and improved survival rates. Traditional diagnostic methods, such as visual inspection and biopsy, often lead to delays and potential misdiagnoses. To address these challenges, deep neural networks (DNNs) have emerged as a powerful tool for automated and accurate melanoma detection. This study explores the application of deep learning models, particularly convolutional neural networks (CNNs), in analyzing dermoscopic images to identify malignant lesions. By leveraging large-scale datasets and advanced image processing techniques, DNNs can achieve high sensitivity and specificity in differentiating melanoma from benign skin conditions. The proposed approach enhances diagnostic efficiency, reduces dependency on subjective clinical assessments, and provides a scalable solution for early melanoma screening. This research highlights the potential of AI-driven methodologies in dermatology and underscores the role of deep learning in revolutionizing cancer diagnosis.