Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification

@article{McIntosh2021PreservationOH,
  title={Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification},
  author={Declan McIntosh and Tunai Porto Marques and Alexandra Branzan Albu},
  journal={2021 18th Conference on Robots and Vision (CRV)},
  year={2021},
  pages={41-48}
}
Chest radiographs are used for the diagnosis of multiple critical illnesses (e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the automatic or semi-automatic analysis of these data are of particular interest. An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists, ultimately allowing for better medical care of lung-, heart- and chest-related conditions. We propose a novel Discrete Wavelet Transform (DWT)-based method for the… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 32 REFERENCES

Automated abnormality classification of chest radiographs using deep convolutional neural networks

TLDR
The remarkable performance in diagnostic accuracy observed in this study shows that deep CNNs can accurately and effectively differentiate normal and abnormal chest radiographs, thereby providing potential benefits to radiology workflow and patient care.

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

TLDR
It is difficult to obtain a large amount of pneumonia dataset for this classification task, so several data augmentation algorithms were deployed to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs.

TLDR
CNNs trained with a modestly sized collection of prospectively labeledchest radiographs achieved high diagnostic performance in the classification of chest radiographs as normal or abnormal; this function may be useful for automated prioritization of abnormal chest radiograph classification.

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

TLDR
A labeler is designed to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation, in CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients.

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

TLDR
An algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists is developed, and it is found that CheXNet exceeds average radiologist performance on the F1 metric.

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

TLDR
A new chest X-rays database, namely ChestX-ray8, is presented, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing, which is validated using the proposed dataset.

Wavelet Integrated CNNs for Noise-Robust Image Classification

TLDR
WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.

ImageNet classification with deep convolutional neural networks

TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.

Tumor detection and classification of MRI brain image using wavelet transform and SVM

  • A. MathewP. B. Anto
  • Computer Science, Medicine
    2017 International Conference on Signal Processing and Communication (ICSPC)
  • 2017
TLDR
The aim of this research work is to propose and implement an efficient system for tumor detection and classification and the different steps involved are image preprocessing for noise removal, feature extraction, segmentation and classification.