An X-ray image-based pruned dense convolution neural network for tuberculosis detection
According to the Ministry of Health in Kenya, tuberculosis (TB) is the fifth greatest cause of death and the main infectious disease killer in Kenya and across the world. In Kenya and throughout Africa, TB continues to wreak havoc on many vulnerable populations, homes, and communities despite being...
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| Format: | Article |
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Elsevier
2024-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990024000363 |
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| author | Edna Chebet Too David Gitonga Mwathi Lucy Kawira Gitonga Pauline Mwaka Saif Kinyori |
| author_facet | Edna Chebet Too David Gitonga Mwathi Lucy Kawira Gitonga Pauline Mwaka Saif Kinyori |
| author_sort | Edna Chebet Too |
| collection | DOAJ |
| description | According to the Ministry of Health in Kenya, tuberculosis (TB) is the fifth greatest cause of death and the main infectious disease killer in Kenya and across the world. In Kenya and throughout Africa, TB continues to wreak havoc on many vulnerable populations, homes, and communities despite being preventable and treatable. Common TB diagnostics, like blood and skin tests, frequently fail to identify the precise kind of TB. As a result, the World Health Organization (WHO) advises expanding the use of X-rays, for screening. In TB-prevalent regions of Kenya, a shortage of radiologists hampers effective screening and diagnosis, highlighting the need for scalable solutions for accurate X-ray analysis.Recent advancements in deep learning techniques have shown promise in the healthcare sector, particularly in radiology. However, many deep convolutional neural network (CNN) architectures are computationally intensive due to their size and resource requirements. This study designed and developed a Pruned CNN to address this issue by applying pruning techniques to baseline architectures. This approach significantly reduced model sizes while maintaining accuracy levels. Specifically, the pruned version of the DenseNet model achieved an impressive 99 % accuracy with a reduction rate of 65.8 %. These results highlight the potential of this pruned CNN as an effective and efficient tool for TB detection, particularly in resource-constrained environments. This study addresses the shortage of radiological expertise in many regions by providing a tool that can assist in the interpretation of X-ray images. This capability can help healthcare providers deliver timely and accurate diagnoses, thereby improving patient care. |
| format | Article |
| id | doaj-art-60ebd0312e674ad0a07aefc323a627be |
| institution | OA Journals |
| issn | 2666-9900 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-60ebd0312e674ad0a07aefc323a627be2025-08-20T02:34:20ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002024-01-01610016910.1016/j.cmpbup.2024.100169An X-ray image-based pruned dense convolution neural network for tuberculosis detectionEdna Chebet Too0David Gitonga Mwathi1Lucy Kawira Gitonga2Pauline Mwaka3Saif Kinyori4Computer Science, Faculty of Science and Technology, Chuka University, Chuka, Kenya; Corresponding author.Computer Science, Faculty of Science and Technology, Chuka University, Chuka, KenyaNursing, School of Nursing and Public Health, Chuka, KenyaComputer Science, Faculty of Science and Technology, Chuka University, Chuka, KenyaComputer Science, Faculty of Science and Technology, Chuka University, Chuka, Kenya; Information Communication and Technology, Faculty of Science and Technology, Chuka University, Chuka, KenyaAccording to the Ministry of Health in Kenya, tuberculosis (TB) is the fifth greatest cause of death and the main infectious disease killer in Kenya and across the world. In Kenya and throughout Africa, TB continues to wreak havoc on many vulnerable populations, homes, and communities despite being preventable and treatable. Common TB diagnostics, like blood and skin tests, frequently fail to identify the precise kind of TB. As a result, the World Health Organization (WHO) advises expanding the use of X-rays, for screening. In TB-prevalent regions of Kenya, a shortage of radiologists hampers effective screening and diagnosis, highlighting the need for scalable solutions for accurate X-ray analysis.Recent advancements in deep learning techniques have shown promise in the healthcare sector, particularly in radiology. However, many deep convolutional neural network (CNN) architectures are computationally intensive due to their size and resource requirements. This study designed and developed a Pruned CNN to address this issue by applying pruning techniques to baseline architectures. This approach significantly reduced model sizes while maintaining accuracy levels. Specifically, the pruned version of the DenseNet model achieved an impressive 99 % accuracy with a reduction rate of 65.8 %. These results highlight the potential of this pruned CNN as an effective and efficient tool for TB detection, particularly in resource-constrained environments. This study addresses the shortage of radiological expertise in many regions by providing a tool that can assist in the interpretation of X-ray images. This capability can help healthcare providers deliver timely and accurate diagnoses, thereby improving patient care.http://www.sciencedirect.com/science/article/pii/S2666990024000363Pruned CNNTuberculosis detectionDensely convolution neural networkDeep learningImage processingPruning |
| spellingShingle | Edna Chebet Too David Gitonga Mwathi Lucy Kawira Gitonga Pauline Mwaka Saif Kinyori An X-ray image-based pruned dense convolution neural network for tuberculosis detection Computer Methods and Programs in Biomedicine Update Pruned CNN Tuberculosis detection Densely convolution neural network Deep learning Image processing Pruning |
| title | An X-ray image-based pruned dense convolution neural network for tuberculosis detection |
| title_full | An X-ray image-based pruned dense convolution neural network for tuberculosis detection |
| title_fullStr | An X-ray image-based pruned dense convolution neural network for tuberculosis detection |
| title_full_unstemmed | An X-ray image-based pruned dense convolution neural network for tuberculosis detection |
| title_short | An X-ray image-based pruned dense convolution neural network for tuberculosis detection |
| title_sort | x ray image based pruned dense convolution neural network for tuberculosis detection |
| topic | Pruned CNN Tuberculosis detection Densely convolution neural network Deep learning Image processing Pruning |
| url | http://www.sciencedirect.com/science/article/pii/S2666990024000363 |
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