Tuberculosis Classification using SVM and Modified CNN
Tuberculosis (TB) is a dangerous disease caused by mycobacterium leads to mortality. Early detection and identification of tuberculosis is crucial for managing tuberculosis infections. Recent technological improvements use a machine learning-based SVM and Modified CNN to identify specific diseases m...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2024-11-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.iust.ac.ir/article-1-3463-en.pdf |
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| author | SRINIVAS BABU N SHASHIKIRAN S M JAYANTHI Rajani N K M PALANISWAMY M R KUSHALATHA |
| author_facet | SRINIVAS BABU N SHASHIKIRAN S M JAYANTHI Rajani N K M PALANISWAMY M R KUSHALATHA |
| author_sort | SRINIVAS BABU N |
| collection | DOAJ |
| description | Tuberculosis (TB) is a dangerous disease caused by mycobacterium leads to mortality. Early detection and identification of tuberculosis is crucial for managing tuberculosis infections. Recent technological improvements use a machine learning-based SVM and Modified CNN to identify specific diseases more accurately, as demonstrated in this research. The modified CNN's improved feature extraction and classification accuracy are maintained throughout construction. To obtain good performance a TBX11K publicly accessible dataset is used it consists of 11000 images of which 4600 chest x-ray (CXR) images are considered in this research, and the suggested model is verified. This approach significantly increases the accuracy of categorizing TB symptoms. The PCA in this system locates the elements and extracts a large amount of variance technique applied to the full chest radiograph for pulmonary tuberculosis identification accuracy using SVM is 93.14% and modified CNN 96.72% respectively. When it comes to helping radiologists diagnose patients and public health professionals screen for tuberculosis in places where the disease is endemic, the proposed system SVM and modified CNN perform better than existing methods. |
| format | Article |
| id | doaj-art-ded56345d4e543cdb7d5e3476b1fa6a4 |
| institution | Kabale University |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-ded56345d4e543cdb7d5e3476b1fa6a42025-01-09T18:47:15ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902024-11-01204126133Tuberculosis Classification using SVM and Modified CNNSRINIVAS BABU N0SHASHIKIRAN S1M JAYANTHI2Rajani N3K M PALANISWAMY4M R KUSHALATHA5 Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, Karnataka India. Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, Karnataka India. Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bangalore, Karnataka India. Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India. Department of Electronics and Communication Engineering, Dr T Thimmaiah Institute of Technology, KGF, Karnataka, India. Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India. Tuberculosis (TB) is a dangerous disease caused by mycobacterium leads to mortality. Early detection and identification of tuberculosis is crucial for managing tuberculosis infections. Recent technological improvements use a machine learning-based SVM and Modified CNN to identify specific diseases more accurately, as demonstrated in this research. The modified CNN's improved feature extraction and classification accuracy are maintained throughout construction. To obtain good performance a TBX11K publicly accessible dataset is used it consists of 11000 images of which 4600 chest x-ray (CXR) images are considered in this research, and the suggested model is verified. This approach significantly increases the accuracy of categorizing TB symptoms. The PCA in this system locates the elements and extracts a large amount of variance technique applied to the full chest radiograph for pulmonary tuberculosis identification accuracy using SVM is 93.14% and modified CNN 96.72% respectively. When it comes to helping radiologists diagnose patients and public health professionals screen for tuberculosis in places where the disease is endemic, the proposed system SVM and modified CNN perform better than existing methods.http://ijeee.iust.ac.ir/article-1-3463-en.pdftuberculosissvmmodified cnn |
| spellingShingle | SRINIVAS BABU N SHASHIKIRAN S M JAYANTHI Rajani N K M PALANISWAMY M R KUSHALATHA Tuberculosis Classification using SVM and Modified CNN Iranian Journal of Electrical and Electronic Engineering tuberculosis svm modified cnn |
| title | Tuberculosis Classification using SVM and Modified CNN |
| title_full | Tuberculosis Classification using SVM and Modified CNN |
| title_fullStr | Tuberculosis Classification using SVM and Modified CNN |
| title_full_unstemmed | Tuberculosis Classification using SVM and Modified CNN |
| title_short | Tuberculosis Classification using SVM and Modified CNN |
| title_sort | tuberculosis classification using svm and modified cnn |
| topic | tuberculosis svm modified cnn |
| url | http://ijeee.iust.ac.ir/article-1-3463-en.pdf |
| work_keys_str_mv | AT srinivasbabun tuberculosisclassificationusingsvmandmodifiedcnn AT shashikirans tuberculosisclassificationusingsvmandmodifiedcnn AT mjayanthi tuberculosisclassificationusingsvmandmodifiedcnn AT rajanin tuberculosisclassificationusingsvmandmodifiedcnn AT kmpalaniswamy tuberculosisclassificationusingsvmandmodifiedcnn AT mrkushalatha tuberculosisclassificationusingsvmandmodifiedcnn |