Research Article Skin Diseases Classification Using Hybrid AI Based Localization Approach Keshetti Sreekala , 1 N. Rajkumar , 2 R. Sugumar, 3 K. V. Daya Sagar , 4 R. Shobarani , 5 K. Parthiban Krishnamoorthy , 6 A. K. Saini, 7 H. Palivela , 8 and A. Yeshitla 9 1 Department of CSE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India 2 Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India 3 Department of Computer Science and Engineering, MITSOE, MITADT University, Pune, India 4 Department of Electronics and Computer Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India 5 Department of Computer Science and Engineering, Dr. M.G.R Educational and Research Institute, Maduravoyal, Chennai, Tamilnadu, India 6 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India 7 Department of Computer Science and Engineering, GBPIET, Pauri Garhwal, Uttarakhand, India 8 Accenture Solutions, Mumbai, Maharashtra, India 9 Department of Biotechnology, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia Correspondence should be addressed to Keshetti Sreekala; ksrikala_cse@mgit.ac.in and A. Yeshitla; alazar.yeshi@aastu.edu.et Received 12 May 2022; Revised 30 July 2022; Accepted 5 August 2022; Published 29 August 2022 Academic Editor: Vijay Kumar Copyright © 2022 Keshetti Sreekala et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. e researcher used a variety of strategies and methods to identify and stop diseases earlier. All of them yield positive results for identifying and categorizing diseases, but proper disease categorization is still lacking. Computer-aided diagnosis is one of the most crucial methods for more accurate disease detection, although it is rarely used in dermatology. For Feature Extraction, we introduced Spectral Centroid Magnitude (SCM). e given dataset is classified using an enhanced convolutional neural network; the first stage of preprocessing uses a median filter, and the final stage compares the accuracy results to the current method. 1. Introduction ere are a lot of hidden problems in the skin that may occur, the diseases are not considered skin diseases, and skin tone is majorly suffered from the ultraviolet rays from the sun. However, dermatologists perform the majority of noninvasive screening tests simply with the naked eye, even though skin illness is a frequent disease for which early detection and classification are essential for patient success and recovery. Due to the ease with which the condition might be missed, this may result in unnecessary diagnostic errors caused by human error. Furthermore, because the symptoms of many common skin diseases share a great deal of similarity, disease classification is challenging [1–4]. Diagnostic procedures need to be precise and timely. rough the application of machine learning algorithms and the utilization of the enormous amount of data present in healthcare facilities and hospitals, artificial intelligence Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 6138490, 7 pages https://doi.org/10.1155/2022/6138490