Skin Cancer Detection through Deep Learning and LBP: An Analytical Study

Skin cancer is among the most widespread and life-threatening forms of cancer, with its prevalence rising globally at an alarming rate. The ability to detect this disease early significantly increases the chances of successful treatment. Unfortunately, when left undiagnosed during its initial stages, skin cancer may progress to metastasis, resulting in high mortality. As a result, the accurate and timely diagnosis of skin cancer has become a major focus in medical research. Recent advancements in artificial intelligence, particularly deep learning, have shown great promise in enhancing diagnostic accuracy. Convolutional Neural Networks (CNNs), along with powerful texture-based feature extraction methods such as Local Binary Pattern (LBP), are at the forefront of these innovations. This review paper presents an analysis of recent research efforts that combine deep learning models and LBP techniques to classify and detect various forms of skin cancer. The study highlights the strengths of different models and techniques used in clinical and academic research.