Introduction: Melanoma Skin Cancer (MSC) is a type of cancer in the human body; therefore, early disease diagnosis is essential for reducing the mortality rate. However, dermoscopic image analysis poses challenges due to factors such as color illumination, light reflections, and the varying sizes and shapes of lesions. To overcome these challenges, an automated framework is proposed in this manuscript. Methods: Initially, dermoscopic...

Non-melanoma skin cancer (NMSC) is among the most prevalent forms of cancer originating in the top layer of the skin, with basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) being its primary categories. While both types are highly treatable, the success of treatment hinges on early diagnosis. Early-stage NMSC detection can be achieved through clinical examination, typically involving visual inspection. An alternative,...

The purpose of this study is development of a novel high-performance low-Petahertz (PHz) biosensor for non-melanoma skin cancer (NMSC) diagnosis. The presented device is designed to work within a microwave imaging regime, which is a promising alternative to conventional diagnostic methods such as visual examination, dermoscopy, and biopsy. The suggested biosensor incorporates a dual-band perfect absorber (operating bands at 0.909...

Malignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...