Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition
In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrins...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/7/2450 |
| _version_ | 1797539014104842240 |
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| author | Fahd Alharithi Ahmed Almulihi Sami Bourouis Roobaea Alroobaea Nizar Bouguila |
| author_facet | Fahd Alharithi Ahmed Almulihi Sami Bourouis Roobaea Alroobaea Nizar Bouguila |
| author_sort | Fahd Alharithi |
| collection | DOAJ |
| description | In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods. |
| first_indexed | 2024-03-10T12:40:20Z |
| format | Article |
| id | doaj.art-271c8adbd5f742b59531674e4b08db2b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:40:20Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-271c8adbd5f742b59531674e4b08db2b2023-11-21T13:56:04ZengMDPI AGSensors1424-82202021-04-01217245010.3390/s21072450Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and RecognitionFahd Alharithi0Ahmed Almulihi1Sami Bourouis2Roobaea Alroobaea3Nizar Bouguila4College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi ArabiaCollege of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi ArabiaCollege of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi ArabiaCollege of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi ArabiaThe Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1T7, CanadaIn this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.https://www.mdpi.com/1424-8220/21/7/2450shifted-scaled Dirichlet distributionmixture modelSVM kernelsdata categorization and recognitionmedical image analysis |
| spellingShingle | Fahd Alharithi Ahmed Almulihi Sami Bourouis Roobaea Alroobaea Nizar Bouguila Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition Sensors shifted-scaled Dirichlet distribution mixture model SVM kernels data categorization and recognition medical image analysis |
| title | Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition |
| title_full | Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition |
| title_fullStr | Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition |
| title_full_unstemmed | Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition |
| title_short | Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition |
| title_sort | discriminative learning approach based on flexible mixture model for medical data categorization and recognition |
| topic | shifted-scaled Dirichlet distribution mixture model SVM kernels data categorization and recognition medical image analysis |
| url | https://www.mdpi.com/1424-8220/21/7/2450 |
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