A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

Kaur, Prabhjot; Harnal, Shilpi; Tiwari, Rajeev; Alharithi, Fahd S.; Almulihi, Ahmed; Noya, Irene Delgado; Goyal, Nitin

doi:10.3390/ijerph182212191

Cited by 34 publications

(17 citation statements)

References 43 publications

(54 reference statements)

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“…[67,70] Many traditional and deep ML models were utilized with the goal of helping to detect COVID-19 infections, complications, or outcomes as one of the most frequent research topic in the last two years. [30,34,[43][44][45][47][48][49]51,52,56,65] The performance of predictive ML models in medicine depends on multiple factors. For challenging prediction problems, the understanding of disease is likely to lead to more accurate prediction.…”

Section: Discussionmentioning

confidence: 99%

“…[38,52,67] The most successful and meaningful application of deep learning ML models was achieved in the imaging field. [53,[55][56][57][58][59][60][61][62][63][64][65] Analyses of CT scans, X-rays, Doppler ultrasound, histo-pathological images obtained high accuracy results, which often outperform medical experts. RNN models capture the temporal nature of EHR, imaging and other medical data to predict diseases, complications, and outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…A dataset of 1,900 chest X-ray images has been used with the proposed CNN based model: "C19D-Net" to detect COVID-19 with the accuracy of 96.24%. [65] The idea is to employ the constructed CNN ML model to help radiologists improve their accuracy of detection of COVID-19 from X-ray images.…”

Section: Logistic Regressionmentioning

confidence: 99%

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Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

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Machine learning (ML) models for analyzing medical data are critical for both accelerating development of novel diagnostic and treatment strategies and improving the accuracy of medical care delivery. Our objective was to comprehensively review supervised ML models for diagnosis or treatment prediction. Publications indexed in PubMed were reviewed to identify articles utilizing supervised predictive ML models in medicine. Articles published between 01/01/2020–01/01/2022 were included in this review. Initially, PubMed was searched using MeSH major terms, and if more extensive search results were needed, a broader search was applied (titles/abstracts).PubMed indexed 21,268 published articles (MeSH Major topic) describing ML methods implemented in medicine. Of those, 11,726 articles were published within the last 2 years. Most of the published ML models in medicine in the last two years were different types of deep learning models (about 75%). Fifty articles were included in this review.Almost all categories of disease were subjects of ML predictions. Positive and negative factors in each of the scenarios need to be evaluated before the most optimal ML model is selected. Domain knowledge and collaborations between physicians and ML experts can improve the selection and prediction performance of ML models in medicine and facilitate implementation in clinical practice. Predictive ML models could provide recommendations to recruit suitable patients for clinical trials. Prediction ML models may contribute to development of more effective diagnostic and therapeutic choices, founded on evidence-based medicine. A broad range of methodological approaches have been taken toward this goal, and those approaches are presented here with their various advantages and disadvantages.AUTHOR SUMMARYOver the last decade, there has been rapid development of Machine learning (ML) methods to analyze Big Data in medicine. ML is aimed to make the computer learn from past experiences and make predictions by recognizing patterns in medical data. We performed a comprehensive systematic literature review of recent publications (last two years), indexed in PubMed/MEDLINE that have described either traditional or deep supervised prediction ML models in medicine. We identified 21,268 articles describing ML implementation in medicine. 11,726 articles were published in the last 2 years. We presented the number of publications describing each of the most often ML methods to show current trends in development of these models. Most of the recently published ML models in medicine were deep learning models. We found that the understanding of disease is likely to lead to more accurate prediction. An important dilemma is the selection of optimal ML models for a specific task, considering amount and type of available data. Domain knowledge and collaborations between physicians and ML experts can improve the prediction performance of ML models, which could help clinicians to select the most effective diagnostic and therapeutic choices available and decrease medical errors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

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“…The two algorithms are compared with respect to the testing accuracy, testing loss, F1 score, Area Under Curve, precision and recall. 13…”

Section: Hardware and Softwarementioning

confidence: 99%

Brain tumour classification of Magnetic resonance images using a novel CNN based Medical Image Analysis and Detection network in comparison with VGG16

Mohan

Ganapathy

Rama

2022

JPTCP

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Aim: This study aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from MRI dataset. The study implemented our novel MIDNet18 CNN architecture in comparison with the VGG16 CNN architecture for classifying normal brain images from the brain tumor images. Materials and methods:The novel MIDNet-18 CNN architecture comprises 14 convolutional layers, 7 pooling layers, 4 dense layers and 1 classification layer. The dataset used for this study has two classes: Normal Brain MR Images and Brain Tumor MR Images. This binary MRI brain dataset consists of 2918 images as training set, 1458 images as validation set and 212 images as test set. Independent sample size calculated was 7 for each group, keeping GPower at 80%. Result: From the experimental results, the proposed MIDNet18 model obtained 98.7% accuracy. Whereas, the VGG16 model obtained an accuracy of 50%. Hence, the performance of the proposed MIDNet18 model achieved is better than VGG16. Conclusion: The proposed model is proved to be statistically significant with p value <0.001 (Independent sample t-test) than the existing model VGG16.Brain tumour classification of magnetic resonance images using a novel CNN-based medical image analysis e114

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“…The inclusion of redundant feature information is one of the drawbacks of employing deep architecture [8]. For classification, Convolutional Neural Network (CNN) [9] architecture incorporates some layers such as ReLu, Max-Pooling, Convolutional, Softmax and Fully Connected Layer [10]. A large amount of data, without any segmentation, is required to train a CNN model.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Kaur

Harnal

Tiwari

et al. 2022

Sensors

Self Cite

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Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

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A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

Cited by 34 publications

References 43 publications

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Brain tumour classification of Magnetic resonance images using a novel CNN based Medical Image Analysis and Detection network in comparison with VGG16

Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

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