Performance Evaluation of a Classification Model for Oral Tumor Diagnosis
9p
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| Idioma: | inglês |
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Computer and Information Science
2024
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| Acesso em linha: | https://ir.oauife.edu.ng/handle/123456789/6516 |
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| _version_ | 1810764572390326272 |
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| author | Awoyelu I. O.1 , Ojo B. R.1 , Aregbesola S. B.2 , & Soyele O. O |
| author_facet | Awoyelu I. O.1 , Ojo B. R.1 , Aregbesola S. B.2 , & Soyele O. O |
| author_sort | Awoyelu I. O.1 , Ojo B. R.1 , Aregbesola S. B.2 , & Soyele O. O |
| collection | DSpace |
| description | 9p |
| format | Article |
| id | oai:ir.oauife.edu.ng:123456789-6516 |
| institution | My University |
| language | English |
| publishDate | 2024 |
| publisher | Computer and Information Science |
| record_format | dspace |
| spelling | oai:ir.oauife.edu.ng:123456789-65162024-06-12T03:00:37Z Performance Evaluation of a Classification Model for Oral Tumor Diagnosis Awoyelu I. O.1 , Ojo B. R.1 , Aregbesola S. B.2 , & Soyele O. O 9p This paper extracted features from region of interest of histopathology images, formulated a classification model for diagnosis, simulated the model and evaluated the performance of the model. This is with a view to developing a histopathology image classification model for oral tumor diagnosis. The input for the classification is the oral histopathology images obtained from Obafemi Awolowo University Dental Clinic histopathology archive. The model for oral tumor diagnosis was formulated using the multilayered perceptron type of artificial neural network. Image preprocessing on the images was done using Contrast Limited Adaptive Histogram Equalization (CLAHE), features were extracted using Gray Level Confusion Matrix (GLCM). The important features were identified using Sequential Forward Selection (SFS) algorithm. The model classified oral tumor diagnosis into five classes: Ameloblastoma, Giant Cell Lesions, Pleomorphic Adenoma, Mucoepidermoid Carcinoma and Squamous Cell Carcinoma. The performance of the model was evaluated using specificity and sensitivity. The result obtained showed that the model yielded an average accuracy of 82.14%. The sensitivity and the specificity values of Ameloblastoma were 85.71% and 89.4%, of Giant Cell Lesions were 83.33% and 94.74%, of Pleomorphic Adenoma were 75% and 95.24%, of Mucoepidermoid Carcinoma were 100% and 100%,and of Squamous Cell Carcinoma were 71.43% and 94.74% respectively. The model is capable of assisting pathologists in making consistent and accurate diagnosis. It can be considered as a second opinion to augment a pathologist’s diagnostic decision. 2024-06-11T13:02:51Z 2024-06-11T13:02:51Z 2020 Article Awoyelu, I. O., Ojo, B. R., Aregbesola, S. B., & Soyele, O. O. (2020). Performance Evaluation of a Classification Model for Oral Tumor Diagnosis. Computer and Information Science, 13(1), 1-1. 1913-8997 10.5539/cis.v13n1p1 https://ir.oauife.edu.ng/handle/123456789/6516 en application/pdf Computer and Information Science |
| spellingShingle | Awoyelu I. O.1 , Ojo B. R.1 , Aregbesola S. B.2 , & Soyele O. O Performance Evaluation of a Classification Model for Oral Tumor Diagnosis |
| title | Performance Evaluation of a Classification Model for Oral Tumor Diagnosis |
| title_full | Performance Evaluation of a Classification Model for Oral Tumor Diagnosis |
| title_fullStr | Performance Evaluation of a Classification Model for Oral Tumor Diagnosis |
| title_full_unstemmed | Performance Evaluation of a Classification Model for Oral Tumor Diagnosis |
| title_short | Performance Evaluation of a Classification Model for Oral Tumor Diagnosis |
| title_sort | performance evaluation of a classification model for oral tumor diagnosis |
| url | https://ir.oauife.edu.ng/handle/123456789/6516 |
| work_keys_str_mv | AT awoyeluio1ojobr1aregbesolasb2soyeleoo performanceevaluationofaclassificationmodelfororaltumordiagnosis |