Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

• Published in: Microscopy research and technique Vol. 84; no. 6; pp. 1272 - 1283
• Main Author: Saba, Tanzila
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2021; Wiley Subscription Services, Inc
• Subjects: Cancer; Computer vision; conventional versus deep learning; Datasets; Deep learning; Diagnosis; Diameters; Fields (mathematics); Fractal geometry; Gabor filters; handcrafted versus non‐handcrafted features; health systems; healthcare; Learning algorithms; Medical diagnosis; Melanoma; Parameter sensitivity; Skin cancer; skin melanoma; health systems; cancer; handcrafted versus non-handcrafted features; skin melanoma; conventional versus deep learning; healthcare
• ISSN: 1059-910X; 1097-0029; 1097-0029
• DOI: 10.1002/jemt.23686

Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non‐handcrafted features. Additionally, clinical features such as Menzies method, seven‐point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted. Computer vision‐based traditional and deep learning techniques for skin cancer diagnosis are explored with handcrafted and non‐handcrafted features, respectively. Results are compared on several benchmark data sets.