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Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification

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Název: Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification
Autoři: null Ashwin Narasimha Murthy
Zdroj: Journal of Electrical Systems. 20:613-624
Informace o vydavateli: Science Research Society, 2024.
Rok vydání: 2024
Témata: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0104 chemical sciences, 3. Good health
Popis: The development and implementation of cost-effective and efficient screening technologies is important. To address these concerns, we have introduced a unique method to detect skin diseases. Each photo is first pre-processed and cropped to pixel size. Six square fields are used to split these pictures into pixels. Techniques for enlarging images, such as rotation, mirroring, and enhancement, are employed to minimize the quantity of parameters needed for further processes. An kernel-weighted fuzzy local information or the C-means clustering model (K-FCM) is used to properly segment cancer-affected regions. Texture and colour features are then extracted. Finally, a deep long-term and short-term memory (DLTM)-based tunicate group algorithm (TSA) is used to detect skin diseases and classify both normal and abnormal classes. The experiment was carried out using MATLAB, and photos were gathered from the Helllev University Hospital in Denmark. According to the comparative analysis results, the proposed DLSTM-TSA outperforms competing products in terms of F-score, sensitivity, and precision.
Druh dokumentu: Article
ISSN: 1112-5209
DOI: 10.52783/jes.3372
Rights: CC BY ND
Přístupové číslo: edsair.doi...........63e08ffb9f6c1ed859ee1a10c2913292
Databáze: OpenAIRE
Popis
Abstrakt:The development and implementation of cost-effective and efficient screening technologies is important. To address these concerns, we have introduced a unique method to detect skin diseases. Each photo is first pre-processed and cropped to pixel size. Six square fields are used to split these pictures into pixels. Techniques for enlarging images, such as rotation, mirroring, and enhancement, are employed to minimize the quantity of parameters needed for further processes. An kernel-weighted fuzzy local information or the C-means clustering model (K-FCM) is used to properly segment cancer-affected regions. Texture and colour features are then extracted. Finally, a deep long-term and short-term memory (DLTM)-based tunicate group algorithm (TSA) is used to detect skin diseases and classify both normal and abnormal classes. The experiment was carried out using MATLAB, and photos were gathered from the Helllev University Hospital in Denmark. According to the comparative analysis results, the proposed DLSTM-TSA outperforms competing products in terms of F-score, sensitivity, and precision.
ISSN:11125209
DOI:10.52783/jes.3372