Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications

Margarat, G. Simi; Hemalatha, G.; Mishra, Annapurna; Shaheen, H.; Maheswari, K.; Tamijeselvan, S; Kumar, U. Pavan; Banupriya, V.; Ferede, Alachew Wubie

doi:10.1155/2022/3357508

Cited by 19 publications

(4 citation statements)

References 27 publications

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“…TB is a serious health problem with a high mortality rate. TB can be completely cured with early diagnosis (Simi Margarat et al, 2022). This study was designed based on CT images of secondary PTB (the most common form of PTB in adults) and included cavitation, tree-in-buds, consolidations, and nodules, and this conclusion was consistent with other studies, especially in active PTB.…”

Section: Resultssupporting

confidence: 66%

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

Liu,

Xie,

et al. 2023

International Journal of Swarm Intelligence Research

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The diagnosis of pulmonary tuberculosis is a complicated process with a long wait. According to the WS 288-2017 standard, PTB can be divided into five types of imaging. To date, no relevant studies on PTB CT images based on the Yolov5 algorithm have been retrieved. To develop an improved strategy YOLOv5, for the classification of PTB lesions based on whole, CT slices were combined with three other modules. CT slices of PTB collected from hospitals were set as training, verification, and external test sets. It is compared with YOLOv5, SSD and RetinaNet neural network methods. The values of precision, recall, MAP, and F1-score of the improved strategy YOLOv5 for the external test were 0.707, 0.716, 0.715, and 0.71. In this study, based on the same dataset, the improved strategy YOLOv5 model has better results than other networks. Our method provides an effective method for the timely detection of PTB.

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Section: Resultssupporting

confidence: 66%

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

Liu,

Xie,

et al. 2023

International Journal of Swarm Intelligence Research

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“…-Optimized feature selection and refinement using MA. [15] -Proposed an IoT-based healthcare application for early TB diagnosis using CXR images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A novel approach to early TB diagnosis through deep learning integration into an Internet of Things (IoT)-based healthcare application is presented in the paper [ 15 ]. The primary screening method, which uses chest X-ray images, acknowledges the significance of early detection in TB therapy.…”

Section: Literature Reviewmentioning

confidence: 99%

TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network

Kim,

Krichen,

Ojo

et al. 2024

Diagnostics

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Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detection recognition with an X-ray image dataset. This paper proposed a novel approach, named the Tuberculosis Segmentation-Guided Diagnosis Model (TSSG-CNN) for Detecting Tuberculosis, using a combined semantic segmentation and adaptive convolutional neural network (CNN) architecture. The proposed approach is distinguished from most of the previously proposed approaches in that it uses the combination of a deep learning segmentation model with a follow-up classification model based on CNN layers to segment chest X-ray images more precisely as well as to improve the diagnosis of TB. It contrasts with other approaches like ILCM, which is optimized for sequential learning, and explainable AI approaches, which focus on explanations. Moreover, our model is beneficial for the simplified procedure of feature optimization from the perspectives of approach using the Mayfly Algorithm (MA). Other models, including simple CNN, Batch Normalized CNN (BN-CNN), and Dense CNN (DCNN), are also evaluated on this dataset to evaluate the effectiveness of the proposed approach. The performance of the TSSG-CNN model outperformed all the models with an impressive accuracy of 98.75% and an F1 score of 98.70%. The evaluation findings demonstrate how well the deep learning segmentation model works and the potential for further research. The results suggest that this is the most accurate strategy and highlight the potential of the TSSG-CNN Model as a useful technique for precise and early diagnosis of TB.

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“…For the process, the respective model has used the Shenzhen China (SC) (Yang et al, 2022 ) and Montgomery County (MC) datasets. The results have shown that the conventional model has attained better accuracy (Simi Margarat et al, 2022 ). Contrarily, the prevailing model has developed CAD for the classification of TB disease using an X-ray dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

YOLOv8's advancements in tuberculosis identification from chest images

Parveen Rahamathulla,

Sam Emmanuel,

Bindhu

et al. 2024

Front. Big Data

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Tuberculosis (TB) is a chronic and pathogenic disease that leads to life-threatening situations like death. Many people have been affected by TB owing to inaccuracy, late diagnosis, and deficiency of treatment. The early detection of TB is important to protect people from the severity of the disease and its threatening consequences. Traditionally, different manual methods have been used for TB prediction, such as chest X-rays and CT scans. Nevertheless, these approaches are identified as time-consuming and ineffective for achieving optimal results. To resolve this problem, several researchers have focused on TB prediction. Conversely, it results in a lack of accuracy, overfitting of data, and speed. For improving TB prediction, the proposed research employs the Selection Focal Fusion (SFF) block in the You Look Only Once v8 (YOLOv8, Ultralytics software company, Los Angeles, United States) object detection model with attention mechanism through the Kaggle TBX-11k dataset. The YOLOv8 is used for its ability to detect multiple objects in a single pass. However, it struggles with small objects and finds it impossible to perform fine-grained classifications. To evade this problem, the proposed research incorporates the SFF technique to improve detection performance and decrease small object missed detection rates. Correspondingly, the efficacy of the projected mechanism is calculated utilizing various performance metrics such as recall, precision, F1Score, and mean Average Precision (mAP) to estimate the performance of the proposed framework. Furthermore, the comparison of existing models reveals the efficiency of the proposed research. The present research is envisioned to contribute to the medical world and assist radiologists in identifying tuberculosis using the YOLOv8 model to obtain an optimal outcome.

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Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications

Cited by 19 publications

References 27 publications

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network

YOLOv8's advancements in tuberculosis identification from chest images

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