Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification

• URL: http://arxiv.org/abs/2503.18973v1
• Date: Sat, 22 Mar 2025 04:35:17 GMT
• Title: Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
• Authors: Muhammad Ahmad, Sardar Usman, Ildar Batyrshin, Muhammad Muzammil, K. Sajid, M. Hasnain, Muhammad Jalal, Grigori Sidorov,
• Abstract summary: Globally, lung-related diseases claim many lives each year, making early and accurate diagnoses crucial.<n>In this study, we utilized a dataset comprising 3,475 chest X-ray images sourced from Mendeley Data.<n>We applied five pre-trained deep learning models, including CNN, ResNet50, DenseNet, CheXNet, and U-Net, as well as two transfer learning algorithms such as Vision Transformer (ViT) and Shifted Window (Swin) to classify these images.
• Score: 4.000123010849776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-related diseases claim many lives each year, making early and accurate diagnoses crucial. Radiographs are valuable tools for the diagnosis of such conditions. The most prevalent lung diseases, including pneumonia, asthma, allergies, chronic obstructive pulmonary disease (COPD), bronchitis, emphysema, and lung cancer, represent significant public health challenges. Early prediction of these conditions is critical, as it allows for the identification of risk factors and implementation of preventive measures to reduce the likelihood of disease onset Methods: In this study, we utilized a dataset comprising 3,475 chest X-ray images sourced from from Mendeley Data provided by Talukder, M. A. (2023) [14], categorized into three classes: normal, lung opacity, and pneumonia. We applied five pre-trained deep learning models, including CNN, ResNet50, DenseNet, CheXNet, and U-Net, as well as two transfer learning algorithms such as Vision Transformer (ViT) and Shifted Window (Swin) to classify these images. This approach aims to address diagnostic issues in lung abnormalities by reducing reliance on human intervention through automated classification systems. Our analysis was conducted in both binary and multiclass settings. Results: In the binary classification, we focused on distinguishing between normal and viral pneumonia cases, whereas in the multi-class classification, all three classes (normal, lung opacity, and viral pneumonia) were included. Our proposed methodology (ViT) achieved remarkable performance, with accuracy rates of 99% for binary classification and 95.25% for multiclass classification.

Related papers

• Deep Learning for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention [17.079190595821494]
  This study concentrates on categorizing three distinct types of lung X-rays: those depicting healthy lungs, those showing lung opacities, and those indicative of viral pneumonia. Five different pre-trained models will be tested on the Lung X-ray Image dataset. Our own model, MobileNet-Lung based on MobileNetV2, was invented to tackle the lung disease classification task and achieved an accuracy of 0.933.
  arXiv  Detail & Related papers  (2024-08-23T16:00:10Z)
• Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance [0.0]
  People all over the globe are affected by pneumonia but deaths due to it are highest in Sub-Saharan Asia and South Asia. In recent years, the overall incidence and mortality rate of pneumonia regardless of the utilization of effective vaccines and compelling antibiotics has escalated. The widespread prevalence of pneumonia has caused the research community to come up with a framework that helps detect, diagnose and analyze diseases accurately and promptly.
  arXiv  Detail & Related papers  (2023-01-20T09:17:39Z)
• A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia [0.0]
  Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people. Deep convolutional neural network to classify plant diseases based on images and tested its performance. DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time.
  arXiv  Detail & Related papers  (2022-12-30T14:37:32Z)
• Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study [75.05049024176584]
  We present a benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images.
  arXiv  Detail & Related papers  (2022-08-29T04:34:15Z)
• Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays Image [11.076902397190961]
  This study explores the effectiveness of established neural network architectures on the pneumonia classification task through the transfer learning paradigm. To present a comprehensive comparison, 12 well-known ImageNet pre-trained models were fine-tuned and used to discriminate among chest-x-rays of healthy people. The experiments were performed using a total of 6330 images split between train, validation and test sets.
  arXiv  Detail & Related papers  (2021-10-06T14:00:18Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images [0.0]
  More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. It is not uncommon to overlook pneumonia detection for a well-trained radiologist, which triggers the need for improvement in the diagnosis's accuracy. We trained, fine-tuned the state-of-the-art deep learning models such as InceptionResNet, MobileNetV2, Xception, DenseNet201, and ResNet152V2 to classify pneumonia accurately.
  arXiv  Detail & Related papers  (2020-11-11T04:50:06Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection [86.81773672627406]
  Clusters of viral pneumonia during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images.
  arXiv  Detail & Related papers  (2020-03-27T11:32:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.