Published Paper Details:

Skin cancer remains one of the most commonly diagnosed cancers worldwide, with its incidence rising steadily due to increased exposure to ultraviolet (UV) radiation and other environmental factors. Early and accurate detection is critical in improving patient survival rates and reducing the burden of invasive treatments. This paper reviews state-of-the-art approaches to skin cancer detection, with a focus on leveraging ML algorithms and advanced image processing techniques. Specifically, it discusses the application of deep learning models, particularly convolutional neural networks (CNNs), in analyzing dermoscopic images for identifying malignant and benign skin lesions such as melanoma, BCC, SCC and other common skin lesions. These methods demonstrate significant potential in achieving high diagnostic accuracy by analyzing features like color, texture, and lesion asymmetry. Furthermore, the paper highlights the challenges faced in developing robust systems, including data diversity, algorithm transparency, and clinical integration. By synthesizing findings from recent advancements, this work underscores the transformative role of AI in dermatology, paving the way for accessible and efficient diagnostic tools to combat the global skin cancer burden.