A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction
Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/10/2438 |
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| author | Anitha K. Radhika S. Kavitha C. Wen-Cheng Lai S. R. Srividhya Naresh K. |
| author_facet | Anitha K. Radhika S. Kavitha C. Wen-Cheng Lai S. R. Srividhya Naresh K. |
| author_sort | Anitha K. |
| collection | DOAJ |
| description | Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost. |
| first_indexed | 2024-03-09T20:38:43Z |
| format | Article |
| id | doaj.art-3828043262944d1289a25593df82f5de |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T20:38:43Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-3828043262944d1289a25593df82f5de2023-11-23T23:03:04ZengMDPI AGBiomedicines2227-90592022-09-011010243810.3390/biomedicines10102438A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and PredictionAnitha K.0Radhika S.1Kavitha C.2Wen-Cheng Lai3S. R. Srividhya4Naresh K.5Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai 603203, IndiaDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602107, IndiaDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, IndiaBachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, TaiwanDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaMedical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.https://www.mdpi.com/2227-9059/10/10/2438LBP variantsimage retrievalfeature indexingFAM classifiersDEFAMNet |
| spellingShingle | Anitha K. Radhika S. Kavitha C. Wen-Cheng Lai S. R. Srividhya Naresh K. A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction Biomedicines LBP variants image retrieval feature indexing FAM classifiers DEFAMNet |
| title | A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction |
| title_full | A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction |
| title_fullStr | A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction |
| title_full_unstemmed | A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction |
| title_short | A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction |
| title_sort | modified lbp operator based optimized fuzzy art map medical image retrieval system for disease diagnosis and prediction |
| topic | LBP variants image retrieval feature indexing FAM classifiers DEFAMNet |
| url | https://www.mdpi.com/2227-9059/10/10/2438 |
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