A deep learning approach for the classification of TB from NIH CXR dataset

Zaidi, Syeda Zainab Yousuf; Akram, M. Usman; Jameel, Amina; Alghamdi, Norah Saleh

doi:10.1049/ipr2.12385

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Recently, there has been a significant increase in the utilization of deep learning models [53]. Alshboul et al [54] used a hybrid mathematical and machine learning prediction approach to evaluate the impact of external support on green building construction costs.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

et al. 2023

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Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

et al. 2023

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“…Automatic TB screening and constructing datasets of annotated CXR images have been significant research findings in TB-related topics [4][5][6][7][8][9]. Recently, the deep learning technique has received great attention due to its ability to perform image classification and organ/lesion segmentation for TB in medical image processing [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. By collecting a large enough dataset, researchers can design a neural-network-based model using a supervised learning/training process.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images

Ou,

Chen,

Chang

et al. 2024

Diagnostics

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We present a deep learning (DL) network-based approach for detecting and semantically segmenting two specific types of tuberculosis (TB) lesions in chest X-ray (CXR) images. In the proposed method, we use a basic U-Net model and its enhanced versions to detect, classify, and segment TB lesions in CXR images. The model architectures used in this study are U-Net, Attention U-Net, U-Net++, Attention U-Net++, and pyramid spatial pooling (PSP) Attention U-Net++, which are optimized and compared based on the test results of each model to find the best parameters. Finally, we use four ensemble approaches which combine the top five models to further improve lesion classification and segmentation results. In the training stage, we use data augmentation and preprocessing methods to increase the number and strength of lesion features in CXR images, respectively. Our dataset consists of 110 training, 14 validation, and 98 test images. The experimental results show that the proposed ensemble model achieves a maximum mean intersection-over-union (MIoU) of 0.70, a mean precision rate of 0.88, a mean recall rate of 0.75, a mean F1-score of 0.81, and an accuracy of 1.0, which are all better than those of only using a single-network model. The proposed method can be used by clinicians as a diagnostic tool assisting in the examination of TB lesions in CXR images.

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Incremental learning-based cascaded model for detection and localization of tuberculosis from chest x-ray images

Vats,

Sharma,

Singh

et al. 2024

Expert Systems with Applications

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A deep learning approach for the classification of TB from NIH CXR dataset

Cited by 7 publications

References 37 publications

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images

Incremental learning-based cascaded model for detection and localization of tuberculosis from chest x-ray images

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