Classification of radiological patterns of tuberculosis with a Convolutional neural network in x-ray images DOI Creative Commons
Adrián Trueba Espinosa,

Jessica Sanchez -Arrazola,

Jair Cervantes

et al.

ELCVIA Electronic Letters on Computer Vision and Image Analysis, Journal Year: 2024, Volume and Issue: 23(1), P. 47 - 59

Published: July 9, 2024

In this paper we propose the classification of radiological patterns with presence tuberculosis in X-ray images, it was observed that two to six (consolidation, fibrosis, opacity, pleural, nodules and cavitations) are present radiographs patients. It is important mention species specialists consider type TB pattern order provide appropriate treatment. should be noted not all medical centres have who can immediately interpret patterns. Considering above, aim classify by means a convolutional neural network help make more accurate diagnosis on X-rays, so doctors recommend immediate treatment thus avoid infecting people. For patterns, proprietary (CNN) proposed compared against VGG16, InceptionV3 ResNet-50 architectures, which were selected based results other radiograph research [1]–[3] . The obtained for Macro-averange AUC-SVM metric architecture 0.80, VGG16 0.75, 0.79. has better results, as does InceptionV3.

Language: Английский

PDF-UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network DOI
Ahmed Iqbal, Muhammad Sharif

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 221, P. 119718 - 119718

Published: Feb. 26, 2023

Language: Английский

Citations

40
Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey DOI
Mohammed A. A. Al‐qaness,

Jie Zhu,

Dalal AL-Alimi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

Citations

10
MMS-Net: Multi-level multi-scale feature extraction network for medical image segmentation DOI Open Access

Chang Zhao,

Wenbing Lv, Xiang Zhang

et al.

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 86, P. 105330 - 105330

Published: Aug. 18, 2023

Language: Английский

Citations

21
Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture DOI Creative Commons
Md. Nahiduzzaman, Md. Omaer Faruq Goni,

Md. Robiul Islam

et al.

Journal of Applied Biomedicine, Journal Year: 2023, Volume and Issue: 43(3), P. 528 - 550

Published: June 26, 2023

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly medically vulnerable patients. In last few decades, new types of lung-related have taken lives millions people, COVID-19 has almost 6.27 million lives. To fight against diseases, timely correct diagnosis with appropriate treatment is crucial in current pandemic. this study, an intelligent recognition system seven been proposed based on machine learning (ML) techniques aid medical experts. Chest X-ray (CXR) images were collected from publicly available databases. A lightweight convolutional neural network (CNN) used extract characteristic features raw pixel values CXR images. The best feature subset identified using Pearson Correlation Coefficient (PCC). Finally, extreme (ELM) perform classification task assist faster reduced computational complexity. CNN-PCC-ELM model achieved accuracy 96.22% Area Under Curve (AUC) 99.48% eight class classification. outcomes demonstrated better performance than existing state-of-the-art (SOTA) models case COVID-19, detection both binary multiclass classifications. For classification, precision, recall fi-score ROC are 100%, 99%, 100% 99.99% respectively demonstrating its robustness. Therefore, overshadowed pioneering accurately differentiate other that can physicians treating patient effectively.

Language: Английский

Citations

19
Enhancing the detection of airway disease by applying deep learning and explainable artificial intelligence DOI
Apeksha Koul, Rajesh K. Bawa,

Yogesh Kumar

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(31), P. 76773 - 76805

Published: Feb. 21, 2024

Language: Английский

Citations

8
SwinUNeLCsT: Global–local spatial representation learning with hybrid CNN–transformer for efficient tuberculosis lung cavity weakly supervised semantic segmentation DOI Creative Commons
Zhuoyi Tan, Hizmawati Madzin, Norafida Bahari

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(4), P. 102012 - 102012

Published: March 28, 2024

Radiological diagnosis of lung cavities (LCs) is the key to identifying tuberculosis (TB). Conventional deep learning methods rely on a large amount accurate pixel-level data segment LCs. This process time-consuming and laborious, especially for those subtle To address such challenges, firstly, we introduce novel 3D TB LCs imaging convolutional neural network (CNN)-transformer hybrid model (SwinUNeLCsT). The core idea SwinUNeLCsT combine local details global dependencies CT scan image feature representation effectively improve recognition ability Secondly, reduce dependence annotations, design an end-to-end weakly supervised semantic segmentation (WSSS) framework. Through this framework, radiologists need only classify number approximate location (e.g., left lung, right or both) in achieve efficient eliminates meticulously drawing boundaries, greatly reducing cost annotation. Extensive experimental results show that outperforms currently popular medical paradigm. Meanwhile, our WSSS framework based also performs best among existing state-of-the-art methods.

Language: Английский

Citations

6
Memory-efficient transformer network with feature fusion for breast tumor segmentation and classification task DOI
Ahmed Iqbal, Muhammad Sharif

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 127, P. 107292 - 107292

Published: Nov. 11, 2023

Language: Английский

Citations

11
OSLTBDNet: Orthogonal softmax layer-based tuberculosis detection network with small dataset DOI
Pradeep Kumar Das,

S Sreevatsav,

Adyasha Sahu

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107584 - 107584

Published: Feb. 8, 2025

Language: Английский

Citations

0
Deep Feature Fusion of Local and Global Patterns for Early Detection of Lung Abnormalities in Chest X-Rays DOI

Ashutosh Awasthi,

Pawan Kumar Tiwari, Dhirendra Verma

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 363 - 377

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays DOI Creative Commons

K. Vanitha,

T R Mahesh, Vijay Kumar

et al.

BMC Medical Imaging, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 24, 2025

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diagnosis from chest X-rays is critical yet challenging due to subtle manifestations of TB, particularly its early stages. Traditional computational methods, primarily using basic convolutional neural networks (CNNs), often require extensive pre-processing and struggle with generalizability across diverse clinical environments. This study introduces novel Vision Transformer (ViT) model augmented Gradient-weighted Class Activation Mapping (Grad-CAM) enhance both diagnostic accuracy interpretability. The ViT utilizes self-attention mechanisms extract long-range dependencies complex patterns directly the raw pixel information, whereas Grad-CAM offers visual explanations decisions about highlighting significant regions X-rays. contains Conv2D stem for initial feature extraction, followed many transformer encoder blocks, thereby significantly boosting ability learn discriminative features without any pre-processing. Performance testing on validation set had an 0.97, recall 0.99, F1-score 0.98 TB patients. On test set, has 0.98, which better than existing methods. addition visuals not only improves transparency but also assists radiologists assessing verifying AI-driven diagnoses. These results demonstrate model's higher precision potential application real-world settings, providing massive improvement automated detection TB.

Language: Английский

Citations

0