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

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Bibliographic Details
Title: Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification
Authors: null Ashwin Narasimha Murthy
Source: Journal of Electrical Systems. 20:613-624
Publisher Information: Science Research Society, 2024.
Publication Year: 2024
Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0104 chemical sciences, 3. Good health
Description: 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.
Document Type: Article
ISSN: 1112-5209
DOI: 10.52783/jes.3372
Rights: CC BY ND
Accession Number: edsair.doi...........63e08ffb9f6c1ed859ee1a10c2913292
Database: OpenAIRE
Description
Abstract: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