Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture

• URL: http://arxiv.org/abs/2404.11843v2
• Date: Fri, 19 Apr 2024 01:45:02 GMT
• Title: Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture
• Authors: Sonit Singh,
• Abstract summary: An automated computer-aided diagnosis system can interpret chest X-rays to augment radiologists by providing actionable insights. In this study, we applied a novel architecture augmenting the DenseNet121 Convolutional Neural Network (CNN) with multi-head self-attention mechanism. Experimental results show that augmenting CNN with self-attention has potential in diagnosing different thoracic diseases from chest X-rays.
• Score: 1.0878040851637998
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical imaging has been used for diagnosis of various conditions, making it one of the most powerful resources for effective patient care. Due to widespread availability, low cost, and low radiation, chest X-ray is one of the most sought after radiology examination for the diagnosis of various thoracic diseases. Due to advancements in medical imaging technologies and increasing patient load, current radiology workflow faces various challenges including increasing backlogs, working long hours, and increase in diagnostic errors. An automated computer-aided diagnosis system that can interpret chest X-rays to augment radiologists by providing actionable insights has potential to provide second opinion to radiologists, highlight relevant regions in the image, in turn expediting clinical workflow, reducing diagnostic errors, and improving patient care. In this study, we applied a novel architecture augmenting the DenseNet121 Convolutional Neural Network (CNN) with multi-head self-attention mechanism using transformer, namely SA-DenseNet121, that can identify multiple thoracic diseases in chest X-rays. We conducted experiments on four of the largest chest X-ray datasets, namely, ChestX-ray14, CheXpert, MIMIC-CXR-JPG, and IU-CXR. Experimental results in terms of area under the receiver operating characteristics (AUC-ROC) shows that augmenting CNN with self-attention has potential in diagnosing different thoracic diseases from chest X-rays. The proposed methodology has the potential to support the reading workflow, improve efficiency, and reduce diagnostic errors.

Related papers

• Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learning [46.75992018094998]
  This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions. High-resolution CXR imaging is crucial for identifying small but critical anomalies, such as nodules or opacities. This paper presents the Multilevel Collaborative Attention Knowledge (MLCAK) method.
  arXiv  Detail & Related papers  (2024-05-22T06:10:54Z)
• Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis [2.15242029196761]
  Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. We propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales.
  arXiv  Detail & Related papers  (2023-04-25T16:56:12Z)
• Improving Chest X-Ray Classification by RNN-based Patient Monitoring [0.34998703934432673]
  We analyze how information about diagnosis can improve CNN-based image classification models. We show that a model trained on additional patient history information outperforms a model trained without the information by a significant margin.
  arXiv  Detail & Related papers  (2022-10-28T11:47:15Z)
• Routine Usage of AI-based Chest X-ray Reading Support in a Multi-site Medical Supply Center [64.91941409801494]
  A commercially available AI solution for the evaluation of Chest X-ray images is able to help radiologists and clinical colleagues 24/7 in a complex environment. The system performs in a robust manner, supporting radiologists and clinical colleagues in their important decisions, in practises and hospitals regardless of the user and X-ray system type producing the image-data.
  arXiv  Detail & Related papers  (2022-10-17T08:06:16Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning [26.031452674698787]
  We propose a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models.
  arXiv  Detail & Related papers  (2021-01-12T02:52:24Z)
• Fused Deep Convolutional Neural Network for Precision Diagnosis of COVID-19 Using Chest X-Ray Images [0.0]
  We propose a computer-aided diagnosis (CAD) to accurately classify chest X-ray scans of COVID-19 and normal subjects by fine-tuning several neural networks. Using k-fold cross-validation and a bagging ensemble, we achieve an accuracy of 99.7% and a sensitivity of 100%.
  arXiv  Detail & Related papers  (2020-09-15T02:27:20Z)
• Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation [0.0]
  We present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSCR of 97.5% on the JSRT dataset.
  arXiv  Detail & Related papers  (2020-03-23T14:45:48Z)

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.