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Lung Tuberculosis Detection using Chest x-ray Images based on Deep Learning Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 32 |
| Year of Publication: 2024 |
| Authors: Addis Meshesha, Getasew Abeba, Sefinew Getnet, Nune Sreenivas |
10.5120/ijca2024923865
|
Addis Meshesha, Getasew Abeba, Sefinew Getnet, Nune Sreenivas . Lung Tuberculosis Detection using Chest x-ray Images based on Deep Learning Approach. International Journal of Computer Applications. 186, 32 ( Aug 2024), 46-58. DOI=10.5120/ijca2024923865
Abstract
Due to the enormous expansion in human population across the globe in the modern period, automatic disease diagnosis has become essential for medical technology. To help radiologists and physicians diagnose and screen patients, as well as to improve diagnostic times and reduce mortality rates, a framework for automated image processing-based illness identification is essential. These days, lung tuberculosis poses a serious risk and is expanding around the world. Using automated detection, identification, and diagnosis systems can help to improve the pace at which diseases are diagnosed and prevent them from spreading over the world, which will help to alleviate this grave issue. The design and development of computer aided diagnosis method will facilitate lung tuberculosis diseases screening. In this work, we conducted an experiment based on adaptive histogram equalization and Gaussian pre-processing with thresholding, active contour model and morphological lung area segmentation approaches integrated with deep learning (Xception stacked with LSTM &ViT-LSTM) enables to feature extract and classify lung tuberculosis chest-xray accurately. The ViT-LSTM network architecture performs better with 93.4% accuracy in comparison with convolutional neural network-based model Xception stacked with LSTM recur-rent neural network with 88% accuracy for detecting lung tuberculosis diseases and also we performed a set of experiments, and the results indicate 41% improvement in average computational speed.
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