Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?

Journal of Imaging ◽

10.3390/jimaging7070105 ◽

2021 ◽

Vol 7 (7) ◽

pp. 105

Author(s):

Guillaume Reichert ◽

Ali Bellamine ◽

Matthieu Fontaine ◽

Beatrice Naipeanu ◽

Adrien Altar ◽

...

Keyword(s):

Deep Learning ◽

Emergency Room ◽

Learning Algorithm ◽

Traumatic Injury ◽

Diagnostic Tools ◽

Appendicular Skeleton ◽

Trauma Patients ◽

X Rays ◽

Fracture Detection ◽

Local Data

The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists.

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Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning

Applied Sciences ◽

10.3390/app11156976 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6976

Author(s):

Miroslav Jaščur ◽

Marek Bundzel ◽

Marek Malík ◽

Anton Dzian ◽

Norbert Ferenčík ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Lung Ultrasound ◽

Video Recording ◽

X Rays ◽

Surgery Complications ◽

Temporal Dimension ◽

Sliding Motion ◽

Post Surgery ◽

Lung Sliding

Certain post-thoracic surgery complications are monitored in a standard manner using methods that employ ionising radiation. A need to automatise the diagnostic procedure has now arisen following the clinical trial of a novel lung ultrasound examination procedure that can replace X-rays. Deep learning was used as a powerful tool for lung ultrasound analysis. We present a novel deep-learning method, automated M-mode classification, to detect the absence of lung sliding motion in lung ultrasound. Automated M-mode classification leverages semantic segmentation to select 2D slices across the temporal dimension of the video recording. These 2D slices are the input for a convolutional neural network, and the output of the neural network indicates the presence or absence of lung sliding in the given time slot. We aggregate the partial predictions over the entire video recording to determine whether the subject has developed post-surgery complications. With a 64-frame version of this architecture, we detected lung sliding on average with a balanced accuracy of 89%, sensitivity of 82%, and specificity of 92%. Automated M-mode classification is suitable for lung sliding detection from clinical lung ultrasound videos. Furthermore, in lung ultrasound videos, we recommend using time windows between 0.53 and 2.13 s for the classification of lung sliding motion followed by aggregation.

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Inception-based Deep Learning Architecture for Tuberculosis Screening using Chest X-rays

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412748 ◽

2021 ◽

Author(s):

Dipayan Das ◽

K.C. Santosh ◽

Umapada Pal

Keyword(s):

Deep Learning ◽

X Rays ◽

Tuberculosis Screening

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3D Image Volumes From 2D Digitally Reconstructed X‐Rays: A Deep Learning Approach In Lower Limb CT‐Scans

Medical Physics ◽

10.1002/mp.14835 ◽

2021 ◽

Author(s):

Diogo F. Almeida ◽

Patricio Astudillo ◽

Dirk Vandermeulen

Keyword(s):

Deep Learning ◽

Lower Limb ◽

Ct Scans ◽

Learning Approach ◽

X Rays ◽

3D Image

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Speedup of deep learning ensembles for semantic segmentation using a model compression technique

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2017.05.004 ◽

2017 ◽

Vol 164 ◽

pp. 16-26 ◽

Cited By ~ 8

Author(s):

Andrew Holliday ◽

Mohammadamin Barekatain ◽

Johannes Laurmaa ◽

Chetak Kandaswamy ◽

Helmut Prendinger

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

Compression Technique ◽

Model Compression ◽

Learning Ensembles

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Application of Deep Learning in Lung Cancer Imaging Diagnosis

Journal of Healthcare Engineering ◽

10.1155/2022/6107940 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Wenfa Jiang ◽

Ganhua Zeng ◽

Shuo Wang ◽

Xiaofeng Wu ◽

Chenyang Xu

Keyword(s):

Lung Cancer ◽

Computer Vision ◽

Deep Learning ◽

Malignant Tumors ◽

Lung Nodule ◽

Ct Images ◽

Lung Nodules ◽

X Rays ◽

Imaging Diagnosis ◽

Risk Of Cancer

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

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Deep Learning of COVID-19 Chest X-Rays: New Models or Fine Tuning?

10.36227/techrxiv.12656948.v1 ◽

2020 ◽

Author(s):

Tuan Pham

Keyword(s):

Deep Learning ◽

High Performance ◽

Data Augmentation ◽

Dominant Role ◽

Care Center ◽

Characteristic Curve ◽

Fine Tuning ◽

Urgent Care ◽

X Rays ◽

Training Time

Chest X-rays have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. While many new DL models have been being developed for this purpose, this study aimed to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases. In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver-operating-characteristic curve. AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

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