A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer.

Simple Summary: Recent advancements in this field have shown continuous improvement, with CAD algorithms enhancing diagnosis accuracy. The study systematically analyzes various aspects of HSI research for skin cancer detection, emphasizing global efforts and the need for more collaboration and data sharing. The results suggest promising advancements in skin cancer identification through HSI, offering potential for early diagnosis and tailored treatments. Integrating CAD algorithms improves diagnostic accuracy. Despite challenges such as limited patient involvement and data availability, HSI offers non-invasive, precise lesion characterization, reducing false positives and negatives. It holds the potential to improve early detection and treatment planning. With ongoing research and collaboration, HSI holds the potential to save lives and improve patient care by facilitating early diagnosis and personalized treatment. Skin cancer, a malignant neoplasm originating from skin cell types including keratinocytes, melanocytes, and sweat glands, comprises three primary forms: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and malignant melanoma (MM). BCC and SCC, while constituting the most prevalent categories of skin cancer, are generally considered less aggressive compared to MM. Notably, MM possesses a greater capacity for invasiveness, enabling infiltration into adjacent tissues and dissemination via both the circulatory and lymphatic systems. Risk factors associated with skin cancer encompass ultraviolet (UV) radiation exposure, fair skin complexion, a history of sunburn incidents, genetic predisposition, immunosuppressive conditions, and exposure to environmental carcinogens. Early detection of skin cancer is of paramount importance to optimize treatment outcomes and preclude the progression of disease, either locally or to distant sites. In pursuit of this objective, numerous computer-aided diagnosis (CAD) systems have been developed. Hyperspectral imaging (HSI), distinguished by its capacity to capture information spanning the electromagnetic spectrum, surpasses conventional RGB imaging, which relies solely on three color channels. Consequently, this study offers a comprehensive exploration of recent CAD investigations pertaining to skin cancer detection and diagnosis utilizing HSI, emphasizing diagnostic performance parameters such as sensitivity and specificity. [ABSTRACT FROM AUTHOR]