CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images

Srivastava, Gaurav; Chauhan, Aninditaa; Jangid, Mahesh; Chaurasia, Sandeep

doi:10.1016/j.bspc.2022.103848

Cited by 31 publications

(7 citation statements)

References 42 publications

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“…The Deep-Chest, combining ResNet and Bi-GRU models, achieved 93.365% accuracy. Srivastava G. et al [40] proposed CoviXNet to perform 3-class classification with an accuracy of 93.36% in CXR images. A DeepCNN with enhanced grey wolf optimization is developed as CXGNet by Gopatoti, A. et al [41] for 4-class classification of CXRs into normal, COVID-19, and pneumonia (viral, bacterial).…”

Section: Related Workmentioning

confidence: 99%

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

Gopatoti

Vijayalakshmi

2023

Biomedical Signal Processing and Control

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Section: Related Workmentioning

confidence: 99%

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

Gopatoti

Vijayalakshmi

2023

Biomedical Signal Processing and Control

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“…It plays a significant role in how CNN operates, which consists of several convolutional filters called kernels. It is a method in which we apply a small number matrix to our image known as a kernel or filter, then transform it using the values of the filter [ 34 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

Ahmed

Mstafa

2022

Diagnostics

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Recently, many diseases have negatively impacted people’s lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, and low-cost computer-based tools for the early prediction of knee OA patients are urgently needed. In this paper, as part of addressing this issue, we developed a new method to efficiently diagnose and classify knee osteoarthritis severity based on the X-ray images to classify knee OA in (i.e., binary and multiclass) in order to study the impact of different class-based, which has not yet been addressed in previous studies. This will provide physicians with a variety of deployment options in the future. Our proposed models are basically divided into two frameworks based on applying pre-trained convolutional neural networks (CNN) for feature extraction as well as fine-tuning the pre-trained CNN using the transfer learning (TL) method. In addition, a traditional machine learning (ML) classifier is used to exploit the enriched feature space to achieve better knee OA classification performance. In the first one, we developed five classes-based models using a proposed pre-trained CNN for feature extraction, principal component analysis (PCA) for dimensionality reduction, and support vector machine (SVM) for classification. While in the second framework, a few changes were made to the steps in the first framework, the concept of TL was used to fine-tune the proposed pre-trained CNN from the first framework to fit the two classes, three classes, and four classes-based models. The proposed models are evaluated on X-ray data, and their performance is compared with the existing state-of-the-art models. It is observed through conducted experimental analysis to demonstrate the efficacy of the proposed approach in improving the classification accuracy in both multiclass and binary class-based in the OA case study. Nonetheless, the empirical results revealed that the fewer multiclass labels used, the better performance achieved, with the binary class labels outperforming all, which reached a 90.8% accuracy rate. Furthermore, the proposed models demonstrated their contribution to early classification in the first stage of the disease to help reduce its progression and improve people’s quality of life.

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“…However, with the rapid growth of deep learning techniques, an increasing number of scholars have applied them to biomedical scenarios and daily health monitoring and achieved desirable results, such as detecting COVID-19 by chest X-ray images [9] and physiological indicator estimation [10] . In particular, deep learning techniques regarding heart rate estimation have matured in physiological indicator estimation.…”

Section: Introductionmentioning

confidence: 99%

Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning

Wang

et al. 2023

Biomedical Signal Processing and Control

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CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images

Cited by 31 publications

References 42 publications

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning

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