Role of Artificial Intelligence for Skin Cancer Detection

• Published in: Evolving Role of AI and IoMT in the Healthcare Market pp. 141 - 174
• Main Authors: Kumar, Piyush, Chauhan, Rishi, Shankar, Achyut, Stephan, Thompson
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Subjects: Artificial Intelligence; Basal cell carcinoma (BCC); Cancer detection; Computer-Aided diagnosis; Convolutional neural network (CNN); Deep learning; Human-computer collaboration; Human-machine interaction; Human–computer interaction (HCI); Image processing; Machine learning; Melanoma; Melanoma skin cancer; Neural networks; Skin cancer; Squamous cell carcinoma (SCC); Tele-health; Tele-medicine; Transfer learning
• ISBN: 3030820785; 9783030820787
• DOI: 10.1007/978-3-030-82079-4_7

Artificial intelligence and its applications are applied in almost every aspect of human life. There is no such paradigm in which its utilization is merely left out. The rapid increase in the usage of Artificial Intelligence has created a bulk of opportunities to consider. Artificial Intelligence has played a vast role in medical grounds, which has led to its use on more and more systems. Artificial intelligence for Skin Cancer diagnosis has supported various dermatologists and clinical experts. This chapter mainly consists of a comparative study of how skin cancer is detected and the study of human–computer interaction (HCI). An image-based AI with high accuracy will benefit clinical expertise to make the right decisions and properly diagnose the patient. Even if a less experienced practitioner is diagnosing the patient, there are high chances to get accurate results. AI in Skin Cancer diagnosis is supported by human–computer interaction or mobile technology environment wherein the clinicians can diagnose, monitor, and recommend skin cancer from a distance. The different machine learning algorithms like support vector machine (SVM), K-Nearest neighbor (KNN), various deep learning algorithms, Fuzzy C-means, etc. are used to detect skin cancer. Different methods of Human–Computer interaction (HCI) like eye-tracking, hand gesture recognition, facial expression recognition, etc., are discussed briefly in this paper. This chapter also outlines how Human–Computer Interaction (HCI) plays an important role in connecting the clinicians to the patients, where all the applications have been previously used, how they can be benefited for Skin Cancer detection, etc. This chapter focuses on solving the problem by minimizing the risk at the first stage of patient–doctor interaction.