Deep learning applied to automatic disease detection using chest X‐rays

Moses, Daniel

doi:10.1111/1754-9485.13273

Cited by 34 publications

(24 citation statements)

References 122 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To overcome this challenge, we resorted to data augmentation techniques by applying zooming (range: 0.5-1.5x), rotation (range: 90 degree), width shifting (range: -10 to 10 pixels), and height shifting (range: -10 to 10 pixels) to the original training dataset (Supplementary Fig. 8) 34 . Together with original samples, the augmented training dataset contained 26,604 images (FCD-WT: 12,897 images, FCD-HT: 13,707 images), which was subsequently subjected to deep learning modeling using the T2D5 architecture.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-based Approaches for Identifying Human Cells Harboring Fetal Chromatin Domain Ablations

Zaheri

Nguyen

et al. 2021

Preprint

View full text Add to dashboard Cite

Two common hemoglobinopathies, sickle cell disease (SCD) and β-thalassemia, arise from genetic mutations within the β-globin gene. A 500-bp motif termed Fetal Chromatin Domain (FCD), upstream of human ϒ-globin locus, may function as a transcriptional regulatory element driving inhibition of the ϒ-globin gene. Here, we hypothesize that the removal of this motif using CRISPR technology may reactivate the expression of ϒ-globin and subsequently restore fetal hemoglobin functionality. In this work we present two different cell morphology-based machine learning approaches that can be used identify cells that harbor FCD genetic modifications. Three candidate models from the first, which uses multilayer perceptron algorithm (MLP 20–26, MLP26-18, and MLP 30 − 26) and flow cytometry-derived cellular data, yielded 0.83 precision, 0.80 recall, 0.82 accuracy, and 0.90 area under the ROC (receiver operating characteristic) curve when predicting the edited cells. In comparison, the candidate model from the second approach, which uses deep learning (T2D5) and DIC microscopy-derived imaging data, performed with less accuracy (0.80) and ROC AUC (0.87). We envision both assays could be valuable and complementary to currently available genotyping protocols for specific genetic modifications which result in morphological changes in human cells.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning-based Approaches for Identifying Human Cells Harboring Fetal Chromatin Domain Ablations

Zaheri

Nguyen

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…According to the radiographic density (depending on the density of the tissue and the atomic number of its components), the structures in the area will absorb the rays differentially, which will result in lights and shadows [31]. In the AI field of computer vision applied to X-ray, there is a preponderance of work in the area of the thoracic cavity [32]. Thus, we found work focused on the detection of pulmonary nodules with models trained on images from one pool of patients and tested in a different pool.…”

Section: Healthcare Applications Of Ai and Computer Vision 81 X-ray I...mentioning

confidence: 99%

The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision

Cossio¹

2023

Artificial Intelligence

View full text Add to dashboard Cite

Diagnostic medical imaging is a key tool in medical care. In recent years, thanks to advances in computer vision research, a subfield of artificial intelligence, it has become possible to use medical imaging to train and test machine learning models. Among the algorithms investigated, there has been a boom in the use of neural networks since they allow a higher level of automation in the learning process. The areas of medical imaging that have developed the most applications are X-rays, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasonography and pathology. In fact, the COVID-19 pandemic has reshaped the research landscape, especially for radiological and resonance imaging. Notwithstanding the great progress that has been observed in the field, obstacles have also arisen that had to be overcome to continue to improve applications. These obstacles include data protection and the expansion of available datasets, which involves a large investment of resources, time and academically trained manpower.

show abstract

“…The weights obtained from the pretrained models have reduced training costs for the new model as it has to learn only a few last layer weights. Deep learning models trained via the transfer learning approaches have shown great potential in diagnosing lung diseases with the ability to classify various etiologies compared to models trained from scratch [ 17 ]. Most of the existing works are based on a ImageNet database pretrained model for lung disease classification using chest X-ray.…”

Section: Introductionmentioning

confidence: 99%

A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images

2022

View full text Add to dashboard Cite

This paper proposes a multichannel deep learning approach for lung disease detection using chest X-rays. The multichannel models used in this work are EfficientNetB0, EfficientNetB1, and EfficientNetB2 pretrained models. The features from EfficientNet models are fused together. Next, the fused features are passed into more than one non-linear fully connected layer. Finally, the features passed into a stacked ensemble learning classifier for lung disease detection. The stacked ensemble learning classifier contains random forest and SVM in the first stage and logistic regression in the second stage for lung disease detection. The performance of the proposed method is studied in detail for more than one lung disease such as pneumonia, Tuberculosis (TB), and COVID-19. The performances of the proposed method for lung disease detection using chest X-rays compared with similar methods with the aim to show that the method is robust and has the capability to achieve better performances. In all the experiments on lung disease, the proposed method showed better performance and outperformed similar lung disease existing methods. This indicates that the proposed method is robust and generalizable on unseen chest X-rays data samples. To ensure that the features learnt by the proposed method is optimal, t-SNE feature visualization was shown on all three lung disease models. Overall, the proposed method has shown 98% detection accuracy for pediatric pneumonia lung disease, 99% detection accuracy for TB lung disease, and 98% detection accuracy for COVID-19 lung disease. The proposed method can be used as a tool for point-of-care diagnosis by healthcare radiologists.Journal instruction requires a city for affiliations; however, this is missing in affiliation 3. Please verify if the provided city is correct and amend if necessary.correct

show abstract

Deep learning applied to automatic disease detection using chest X‐rays

Cited by 34 publications

References 122 publications

Machine Learning-based Approaches for Identifying Human Cells Harboring Fetal Chromatin Domain Ablations

Machine Learning-based Approaches for Identifying Human Cells Harboring Fetal Chromatin Domain Ablations

The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision

A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images

Contact Info

Product

Resources

About