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Author

Deepak Gupta

Bio: Deepak Gupta is an academic researcher from Maharaja Agrasen Institute of Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 30, co-authored 351 publications receiving 3615 citations. Previous affiliations of Deepak Gupta include Inatel & Guru Gobind Singh Indraprastha University.


Papers
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Journal ArticleDOI
TL;DR: This research presents a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN and demonstrates that the synthetic images produced by this model can be utilized to enhance the performance of CNN for COVID-19 detection.
Abstract: Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.

505 citations

Journal ArticleDOI
TL;DR: A novel deep learning framework for the detection of pneumonia using the concept of transfer learning, where features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction.
Abstract: Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

417 citations

Journal ArticleDOI
TL;DR: The proposed identification model is based on Mask-RCNN, a deep neural network which incorporates global and local features for pixel-wise segmentation which achieves robustness through critical modifications of the training process and a novel post-processing step which merges bounding boxes from multiple models.

291 citations

Journal ArticleDOI
TL;DR: A deep learning-based automated detection and classification model for fundus DR images that offers better classification over the existing models is proposed.

164 citations

Journal ArticleDOI
TL;DR: The novelty and objective of this proposed model as feature selection, it’s used to enhance the performance of classifying process with the help of improved gray wolf optimization.
Abstract: Thyroid diseases are across the board around the world. In India as well, there is a critical issue caused because of this disease. Different research studies estimate that around 42 million individuals in India suffer from the ill effects of “thyroid diseases.” Diagnosis of health situations is an energetic and testing undertaking in the field of therapeutic science. Our proposed model is to classify this thyroid data utilizing optimal feature selection and kernel-based classifier process. We will create classifications models and its group show for classification of data using “multi kernel support vector machine.” The novelty and objective of this proposed model as feature selection, it’s used to enhance the performance of classifying process with the help of improved gray wolf optimization. Reason for this optimal feature selection as the insignificant features from unique dataset and computationally increment the performance of the model. The proposed thyroid classification results in the accuracy, sensitivity, and specificity of 97.49, 99.05 and 94.5%, compared to the existing model. This performance measure is computed from the confusion matrix with the diverse measures contrasted with individual models and in addition to the existing classifier and optimization techniques.

159 citations


Cited by
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01 Jan 2002

9,314 citations

01 Feb 2009
TL;DR: This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale, and what might be coming next.
Abstract: Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

5,234 citations

Reference EntryDOI
15 Oct 2004

2,118 citations