Bio-inspired deep-CNN pipeline for skin cancer early diagnosis

Skin cancer is the most common type of cancer, as also among the riskiest in the medical oncology field. Skin cancer is more common in people who work or practice outdoor sports and those that expose themselves to the sun. It may also develop years after radiographic therapy or exposure to substances that cause cancer (e.g., arsenic ingestion). Numerous tumors can aect the skin, which is the largest organ in our body and is made up of three layers: the epidermis (superficial layer), the dermis (middle layer) and the subcutaneous tissue (deep layer). The epidermis is formed by dierent types of cells: melanocytes, which have the task of producing melanin (a pigment that protects against the damaging eects of sunlight), and the more numerous keratinocytes. The keratinocytes of the deepest layer are called basal cells and can give rise to basal cell carcinomas. We are interested in types of skin cancer that originate from melanocytes, i.e., the so-called melanomas, because it is the most aggressive. The dermatologist, during a complete visit, evaluates the personal and family history of the patient and carries out an accurate visual examination of the skin, thanks to the use of epi-luminescence (or dermoscopy), a special technique for enlarging and illuminating the skin. This paper mentions one of the most widely used diagnostic methods due to its simplicity and validity—the ABCDE method (Asymmetry, edge irregularity, Color Variegation, Diameter, Evolution). This methodology, based on “visual” investigation by the dermatologist and/or oncologist, has the advantage of not being invasive and quite easy to perform. This approach is aected by the opinion of who (physicians) applies it. For this reason, certain diagnosis of cancer is made, however, only with a biopsy, a procedure during which a portion of tissue is taken and then analyzed under a microscope. Obviously, this is particularly invasive for the patient. The authors of this article have analyzed the development of a method that obtains with good accuracy the early diagnosis of skin neoplasms using non-invasive, but at the same time, robust methodologies. To this end, the authors propose the adoption of a deep learning pipeline based on morphological analysis of the skin lesion. The results obtained and compared with previous approaches confirm the good performance of the proposed pipeline.