Automated Classification of Breast Parenchymal Density: Topologic Analysis of X-Ray Attenuation Patterns Depicted with Digital Mammography

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

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Automated Classification of Osteosarcoma and Benign Tumors using RNA-seq and Plain X-ray

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ◽

10.1109/embc44109.2020.9176104 ◽

2020 ◽

Author(s):

Olivia Alge ◽

Lu Lu ◽

Zhi Li ◽

Yingqi Hua ◽

Jonathan Gryak ◽

...

Keyword(s):

Benign Tumors ◽

Automated Classification ◽

Rna Seq ◽

X Ray

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COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Applied Intelligence ◽

10.1007/s10489-020-01867-1 ◽

2020 ◽

Author(s):

Tahmina Zebin ◽

Shahadate Rezvy

Keyword(s):

Disease Progression ◽

Input Image ◽

Mapping Technique ◽

Detection Accuracy ◽

X Rays ◽

Automated Classification ◽

X Ray ◽

Chest X Ray ◽

Activation Mapping

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages.

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Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

Applied Sciences ◽

10.3390/app112210528 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10528

Author(s):

Khin Yadanar Win ◽

Noppadol Maneerat ◽

Syna Sreng ◽

Kazuhiko Hamamoto

Keyword(s):

Deep Learning ◽

Area Under Curve ◽

Ensemble Classifier ◽

Saliency Map ◽

X Rays ◽

Automated Classification ◽

Deep Convolutional Neural Networks ◽

X Ray ◽

Chest X Ray

The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.

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