Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection

Jee, Gaurav; Harshvardhan, GM; Gourisaria, Mahendra Kumar

doi:10.1080/09720502.2020.1838061

Cited by 21 publications

(10 citation statements)

References 15 publications

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“…The feature map is diminished by 50 percent, after each pooling layer. The last pooling layer has 7 × 7 with 512 channels [ 32 ], [ 33 ]. The network comprises total of 14, 714, 688 parameters.…”

Section: Methodology and Materialsmentioning

confidence: 99%

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Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks

et al. 2022

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Childlessness or infertility among couples has become a global health concern. Due to the rise in infertility, couples are looking for medical supports to attain reproduction. This paper deals with diagnosing infertility among men and the major factor in diagnosing infertility among men is the Sperm Morphology Analysis (SMA). In this manuscript, we explore establishing deep learning frameworks to automate the classification problem in the fertilization of sperm cells. We investigate the performance of multiple state-of-the-art deep neural networks on the MHSMA dataset. The experimental results demonstrate that the deep learning-based framework outperforms human experts on sperm classification in terms of accuracy, throughput and reliability. We further analyse the sperm cell data by visualizing the feature activations of the deep learning models, providing a new perspective to understand the data. Finally, a comprehensive analysis is also demonstrated on the experimental results obtained and attributing them to pertinent reasons.

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Section: Methodology and Materialsmentioning

confidence: 99%

“…× 7 with 512 channels[32],[33]. The network comprises total of 14, 714, 688 parameters.VOLUME , 2020…”

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confidence: 99%

Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks

et al. 2022

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“…They concluded that fewer features yielded better results because of less chances of overfitting and hence used the single feature for analysis. Jee et al [30] studied the inference capabilities of neural network models using chest radiographs for the purpose of Covid detection which showed an accuracy of 98% using CNN. Transfer learning architecture was used by Kermany [25] to diagnose paediatric pneumonia using chest X-rays, moreover differentiate between viral and bacterial pneumonia using 5232 X-ray images further labelled as viral, bacterial and normal.…”

Section: Related Workmentioning

confidence: 99%

Distinguishing Pneumonia and COVID-19: Utilizing Computer Vision to Mimic Clinician Efficacy

Saha

Gourisaria

Harshvardhan

2021

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)

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Corona Virus has caused a disruption to the normalcy in the world and require thorough patient management. Looking at the present scenario and the kind of pandemic that it has turned out to be, this paper aims at providing an enhancement to the RT-PCR way of testing and uses the chest X-Rays to detect the presence and severity of the corona virus in a body to successfully differentiate between pneumonia and the Covid-19. The main motive is to help doctors and medical experts with an advance aid to the nursing of critically affected patients. This is feasible because the X-Ray machines are widely available throughout the country and they can assist in the advanced detection of the disease. Around 6000 images of the three kinds of chest X-Rays of patients with pneumonia, Covid, as well as completely normal patients, were used in the process. The paper concludes with the explicit comparison of all the models and their results. Primarily, a simple CNN model was opted for the scrutiny and then later on VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2 pre-trained models were utilised for anatomizing Covid-19 with respect to pneumonia and normal cases. In case of CNN, the maximum accuracy that was attained was 95.30% whereas, for the VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2, the maximum accuracies in correctly predicting the diseases were 95.63% , 96.02% , 94.82% , 95.23% and 93.39% respectively.

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“…There are many applications of CNN architectures decoding paddy crop disease detection [25], COVID-19 detection [26], facial recognition [27], and nuclei segmentation [28,29]. CNNs are composed of four main types of layers-convolutional, Maxpooling, flattening, and full connection.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Mycobacterium Tuberculosis Detection Using CNN Ranking Approach

Singh

Gourisaria

Harshvardhan

et al. 2021

Advances in Intelligent Systems and Computing

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The main reason behind the large number of mortality among homosapiens is due to Mycobacterium tuberculosis (TB). Majorly, TB can be classified into two categories named as active and latent tuberculosis. Tuberculosis diagnosis is a critical area and needs a high extent of accuracy for working. Minor errors in diagnosis can result in disastrous repercussions. The ultimate goal of this paper is to detect TB at right time and help combat the increasing number of cases by early treatment. In this paper, we implement CNNs of different configurations on a dataset for binary classification and mainly focus on the objective function value obtained from different CNN models with an LVCEL, MVA, and TT. The objective function can be a useful tool for physicians and our medical community for correctly identifying a TB patient. Experimental evaluation of the best architecture shows that a maximum objective function value of 6.503, with a validation accuracy score of 0.9671 and an AUC of 0.9733 in the receiver-operating characteristics (ROC) curve, is achieved to correctly identify whether TB is present or not.

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Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection

Abstract: Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection

Cited by 21 publications

References 15 publications

Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks

Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks

Distinguishing Pneumonia and COVID-19: Utilizing Computer Vision to Mimic Clinician Efficacy

Mycobacterium Tuberculosis Detection Using CNN Ranking Approach

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