Tuberculosis Classification using SVM and Modified CNN

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Babu N. Srinivas; S. Shashikiran; Jayanthi M.; N. Rajani; Palaniswamy K. M.; Kushalatha M. R.

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING Year:2024 | Volume:20 | Issue:4 Page(s): 126-133

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Title

Tuberculosis Classification using SVM and Modified CNN

Author(s)

Keywords

Tuberculosis

SVM

Modified CNN

Abstract

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.

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