Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)                   IJEEE 2024, 20(4): 126-133 | Back to browse issues page

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BABU N S, S S, JAYANTHI M, N R, PALANISWAMY K M, KUSHALATHA M R. Tuberculosis Classification using SVM and Modified CNN. IJEEE 2024; 20 (4) :126-133
URL: http://ijeee.iust.ac.ir/article-1-3463-en.html
Tuberculosis Classification using SVM and Modified CNN
SRINIVAS BABU N , SHASHIKIRAN S , M JAYANTHI , Rajani N , K M PALANISWAMY , M R KUSHALATHA
Abstract:   (290 Views)
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.
Keywords: Tuberculosis, SVM, Modified CNN
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Type of Study: Closed - 2024 Special Issue on Applications of Deep Learning in Electrical and Electronic Engineerin | Subject: Biomedical Signal Processing
Received: 2024/09/23 | Revised: 2025/01/03 | Accepted: 2024/12/01
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.