Tuberculosis Detection from Chest Radiographs with Pre-trained Deep Learning Scheme: A Study

Seifedine Kadry, V. Rajinikanth, A. Chandrasekar, A. Nandhini
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Abstract

In human, the abnormality in lung causes a severe respiratory problem and breathing difficulties. Tuberculosis (TB) is one of the common lung abnormality caused due a bacterium named Mycobacterium tuberculosis. TB infection will cause harsh breathing issues and untreated TB will lead to death. Detection of the TB using Chest X-ray is one of the common techniques and in this research work; TB detection using Deep-Learning-Technique (DLT) is demonstrated. The various phases of this research involve; (i) Image collection and resizing, (ii) Deep-Feature (DF) extraction with chosen technique, (iii) Classification using SoftMax classifier and (iv) Performance evaluation with existing technique. The proposed work employs a 5-fold cross validation and the best value is considered as the result. The outcome of this study confirms that, the classification accuracy achieved with ResNetl8 and K-Nearest Neighbor (ResNet+KNN) offered better outcome >97% compared to other DLT of this study.
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基于预训练深度学习方案的胸片结核检测研究
在人类中,肺的异常会导致严重的呼吸问题和呼吸困难。结核(TB)是由结核分枝杆菌引起的常见肺部异常之一。结核病感染将导致严重的呼吸问题,未经治疗的结核病将导致死亡。胸部x线检测结核病是目前研究工作中常用的技术之一;演示了使用深度学习技术(DLT)检测结核病。这项研究的各个阶段包括;(i)图像收集和调整大小,(ii)选定技术的深度特征(DF)提取,(iii)使用SoftMax分类器进行分类,(iv)使用现有技术进行性能评估。提出的工作采用5倍交叉验证和最佳值被认为是结果。本研究的结果证实,与本研究的其他DLT相比,resnet18和k -最近邻(ResNet+KNN)的分类准确率达到了97%以上。
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