Accurate Integrated System to detect Pulmonary and Extra Pulmonary Tuberculosis using Machine Learning Algorithms

Kaur, Rupinder; Sharma, Anurag

doi:10.4114/intartif.vol24iss68pp104-122

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…It has employed clinical and imaging data from hospitals and incorporated the Decision Tree algorithm. The result has shown that the Decision Tree (DT) algorithm has attained 95.5% classification accuracy (Kaur and Sharma, 2021 ). In the same way, the existing model has demonstrated a TB detection mechanism (Heyckendorf et al, 2021 ) utilizing advanced DL models.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

“…Several existing studies have been focused on predicting TB and non-TB. However, limited research has focused on the ATB, healthy, LTBI, sick, and non-TB classifications (Kaur and Sharma, 2021 ).…”

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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“…By examining several computed tomography (CT) scans [5], researchers have found that early detection of lung cancinoma [6] using pattern analysis can save lives. Therefore, starting in the early 1980s, computer-aided diagnosis (CAD) systems were used to aid clinicians in efficiently interpreting medical images [7,8].…”

Section: Introductionmentioning

confidence: 99%

Classification of Benign and Malignancy in Lung Cancer Using Capsule Networks with Dynamic Routing Algorithm on Computed Tomography Images

Bushara

Kumar

2023

JAIT

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There is a widespread agreement that lung cancer is one of the deadliest types of cancer, affecting both women and men. As a result, detecting lung cancer at an early stage is crucial to create an accurate treatment plan and forecasting the reaction of the patient to the adopted treatment. For this reason, the development of Convolutional Neural Networks (CNNs) for the task of lung cancer classification has recently seen a trend in attention. CNNs have great potential, but they need a lot of training data and struggle with input alterations. To address these limitations of CNNs, a novel machine-learning architecture of capsule networks has been presented, and it has the potential to completely transform the ares of deep learning. Capsule networks, which are the focus of this work, are interesting because they can withstand rotation and affine translation with relatively little training data. This research optimizes the performance of CapsNets by designing a new architecture that allows them to perform better on the challenge of lung cancer classification. The findings demonstrate that the proposed Capsule Network method outperforms CNNs on the lung cancer classification challenge. CapsNet with a single convolution layer and 32 features (CN-1-32), CapsNet with a single convolution layer and 64 features (CN-1-64), and CapsNet with a double convolution layer and 64 features (CN-2-64) are the three Capsulel networks developed in this research for lung cancer classification. Lung nodules, both benign and malignant, are classified using these networks using CT images. The LIDC-IDRI database was utilized to assess the performance of those networks. Based on the testing results, CN-2-64 network performed the better out of the three networks tested, with a specificity of 98.37%, sensitivity of 97.47% and an accuracy of 97.92%.

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