Multi-Class Skin Cancer Detection Using Fusion of Textural Features Based CAD Tool.

Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade. The diagnosis of skin cancer is of paramount importance, yet the process is intricate and challenging. The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature, the visibility of dense hair, and the presence of various substitute characteristics. The texture of the epidermis is notably different from that of normal skin, and these differences are often evident in cases of unhealthy skin. As a consequence, the development of an effective method for monitoring skin cancer has seen little progress. Moreover, the task of diagnosing skin cancer from dermoscopic images is particularly challenging. It is crucial to diagnose skin cancer at an early stage, despite the high cost associated with the procedure, as it is an expensive process. Unfortunately, the advancement of diagnostic techniques for skin cancer has been limited. To address this issue, there is a need for a more accurate and efficient method for identifying and categorizing skin cancer cases. This involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer occurrences. We present and evaluate several techniques for segmentation, categorized into three main types: thresholding, edge-based, and region-based. These techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano (PH2) collection. The evaluation is based on twelve distinct metrics, which are designed to measure various types of errors with particular clinical significance. Additionally, we assess the effectiveness of these techniques independently for three different types of lesions: melanocytic nevi, atypical nevi, and melanomas. The first technique is capable of classifying lesions into two categories: atypical nevi and melanoma, achieving the highest accuracy score of 90.00% with the Otsu (3-level) method. The second technique also classifies lesions into two categories: common nevi and melanoma, achieving a score of 90.80% with the Binarized Sauvola method. [ABSTRACT FROM AUTHOR]