Browsing by Author "Alok Tiwari"

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Deep learning-based automated multiclass classification of chest X-rays into Covid-19, normal, bacterial pneumonia and viral pneumonia
(Cogent OA, 2022) Alok Tiwari; Taresh Sarvesh Sharan; Shiru Sharma; Neeraj Sharma
Covid-19 has been a pandemic across almost all parts of the world. Due to its higher spread rate and increased mortality rate, early detection of this is required. In the present study, we have used chest X-Rays to identify the presence of Covid-19 and several other Pneumonia types (Viral and Bacterial). To perform this classification, we have used a transfer learning-based model relying upon a pre-trained VGG-16 classifier network. Along with that, we have used the inception module as a pre-processing cursor to this network. We present our model via three case study approaches, namely–Case (01)–four-class classification, Case (02)–three-class classification, and Case (03)–two-class classification. For these case studies, we have selected our classes from Normal, Covid-19, Viral Pneumonia, and Bacterial Pneumonia. We have evaluated our model’s classification performance on various parameters, such as—accuracy, precision, sensitivity, specificity, false-positive rate, and F1-score, as just one parameter is not sufficient enough to evaluate the performance. After training the network for all three cases, we have found Covid-19 classification accuracies–Case 01–91.86% (Four Classes), Case 02–97.67% (Three Classes), and Case 03–99.61% (Two Classes) and all the other parameters are well represented in the performance parameter section. Our proposed model testing accuracies for all three cases are–Case 01–87.32% (Four Classes), Case 02–96.89% (Three Classes), and Case 03–99.95% (Two Classes). Based on the achieved accuracies, our model showed comparable performance to pre-existing methods like VGG-16, Res-Net, and Inception-Net. We can use these case studies for the interpretation and classification of chest X-Rays in these classes and with increased dataset and computational power, we can apply the proposed model for more class classification. © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach
(IOS Press BV, 2022) Alok Tiwari; Sumit Tripathi; Dinesh Chandra Pandey; Neeraj Sharma; Shiru Sharma
BACKGROUND: The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. OBJECTIVE: The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. METHODS: The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. RESULT: The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. CONCLUSION: The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection. © 2022 - IOS Press. All rights reserved.