Skin Diseases Classification Using Hybrid AI Based Localization Approach.

Autor: Sreekala K; Department of CSE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India., Rajkumar N; Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India., Sugumar R; Department of Computer Science and Engineering, MITSOE, MITADT University, Pune, India., Sagar KVD; Department of Electronics and Computer Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India., Shobarani R; Department of Computer Science and Engineering, Dr. M.G.R Educational and Research Institute, Maduravoyal, Chennai, Tamilnadu, India., Krishnamoorthy KP; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India., Saini AK; Department of Computer Science and Engineering, GBPIET, Pauri Garhwal, Uttarakhand, India., Palivela H; Accenture Solutions, Mumbai, Maharashtra, India., Yeshitla A; Department of Biotechnology, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Aug 29; Vol. 2022, pp. 6138490. Date of Electronic Publication: 2022 Aug 29 (Print Publication: 2022).
DOI: 10.1155/2022/6138490
Abstrakt: 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. The 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). The 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.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Keshetti Sreekala et al.)
Databáze: MEDLINE
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