Classification of Chest Diseases using Wavelet Transforms and Transfer Learning

• URL: http://arxiv.org/abs/2002.00625v1
• Date: Mon, 3 Feb 2020 09:44:23 GMT
• Title: Classification of Chest Diseases using Wavelet Transforms and Transfer Learning
• Authors: Ahmed Rasheed, Muhammad Shahzad Younis, Muhammad Bilal, and Maha Rasheed
• Abstract summary: Our system combines the techniques of image processing for feature enhancement and deep learning for classification among diseases. We have used the ChestX-ray14 database in order to train our deep learning model on the 14 different labeled diseases found in it.
• Score: 1.5997248501926518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chest X-ray scan is a most often used modality by radiologists to diagnose many chest related diseases in their initial stages. The proposed system aids the radiologists in making decision about the diseases found in the scans more efficiently. Our system combines the techniques of image processing for feature enhancement and deep learning for classification among diseases. We have used the ChestX-ray14 database in order to train our deep learning model on the 14 different labeled diseases found in it. The proposed research shows the significant improvement in the results by using wavelet transforms as pre-processing technique.

Related papers

• SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification [0.6218519716921521]
  We employ deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%.
  arXiv  Detail & Related papers  (2023-11-13T21:07:07Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Detection of COVID19 in Chest X-Ray Images Using Transfer Learning [0.0]
  This paper investigates the concept of transfer learning using two of the most well-known VGGNet architectures, namely VGG-16 and VGG-19. We generated two different datasets to evaluate the performance of the proposed system for the identification of positive Covid-19 instances in a multiclass and binary classification problems.
  arXiv  Detail & Related papers  (2023-04-09T05:02:04Z)
• 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)
• 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)
• Interpretation of Chest x-rays affected by bullets using deep transfer learning [0.8189696720657246]
  Deep learning in radiology provides the opportunity to classify, detect and segment different diseases automatically. In the proposed study, we worked on a non-trivial aspect of medical imaging where we classified and localized the X-Rays affected by bullets. This is the first study on the detection and classification of radiographs affected by bullets using deep learning.
  arXiv  Detail & Related papers  (2022-03-25T05:53:45Z)
• 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)
• Self-supervised deep convolutional neural network for chest X-ray classification [0.0]
  We propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. The results obtained on four public datasets show that our approach yields competitive results without requiring large amounts of labeled training data.
  arXiv  Detail & Related papers  (2021-03-04T14:28:37Z)
• Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets [55.06983249986729]
  We show that a deep learning model performing well when tested on the same dataset as training data starts to perform poorly when it is tested on a dataset from a different source. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation.
  arXiv  Detail & Related papers  (2020-08-04T07:41:15Z)
• Deep learning classification of chest x-ray images [0.0]
  We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.
  arXiv  Detail & Related papers  (2020-05-19T17:29:33Z)
• Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations [70.0118756144807]
  This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation and segmentation of radiographs. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2%, compared to 27.0% and 34.9% respectively in control images.
  arXiv  Detail & Related papers  (2020-05-08T02:16:17Z)

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.