In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays

• URL: http://arxiv.org/abs/2104.02238v1
• Date: Tue, 6 Apr 2021 02:01:43 GMT
• Title: In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays
• Authors: Alexandrea K. Ramnarine
• Abstract summary: 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.
• Score: 91.3755431537592
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to indirectly aid in infection containment, however generating a reliably labeled image dataset for a novel disease is not an easy feat, nor is it of highest priority when combating a global pandemic. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states for other lung pathologies; this study aims to leverage that body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Furthermore, this study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.

Related papers

• Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images [2.232567376976564]
  Since 2019, the global dissemination of the Coronavirus and its novel strains has resulted in a surge of new infections. The use of X-ray and computed tomography (CT) imaging techniques is critical in diagnosing and managing COVID-19. This paper focuses on the methodological approach of using machine learning (ML) to enhance medical imaging for COVID-19 diagnosis.
  arXiv  Detail & Related papers  (2024-01-13T09:17:39Z)
• Multi-Label Chest X-Ray Classification via Deep Learning [0.0]
  The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.
  arXiv  Detail & Related papers  (2022-11-27T20:27:55Z)
• COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks [0.0]
  We developed a computer solution to support vision in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95.
  arXiv  Detail & Related papers  (2021-12-14T01:59:25Z)
• 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)
• Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks [6.420262246029286]
  We propose a novel Graph Convolution Neural Network (GCN) that is capable of identifying bio-markers of Covid-19 pneumonia. The proposed method exploits important relational knowledge between data instances and their features using graph representation and applies convolution to learn the graph data.
  arXiv  Detail & Related papers  (2021-05-20T13:13:29Z)
• 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)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review [10.153806948106684]
  Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML)
  arXiv  Detail & Related papers  (2020-07-16T16:04:17Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)

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.