Faculty Publications
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Item Image Manipulation Detection Using Augmentation and Convolutional Neural Networks(Springer Science and Business Media Deutschland GmbH, 2024) Maheshwari, A.; Jain, R.; Mahapatra, R.; Palakuru, S.; Anand Kumar, M.A.Image tampering is now simpler than ever, thanks to the explosion of digital photos and the creation of easy image modification tools. As a result, if the situation is not handled properly, the major problems may arise. Many computer vision and deep learning strategies have been put out over the years to address the problem. Having said that, people can easily recognize the photographs that were used in that research. This begs the key question of how CNNs might do on more difficult samples. In this chapter, we build a complex CNN network and use various machine learning algorithms to classify the images and compare the accuracies obtained by them. Its performance is also compared on two different datasets. Additionally, we assess the impact of various hyperparameters and a data augmentation strategy on classification performance. This leads to a conclusion that performance can be considerably impacted by dataset difficulty. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item COVID-19: Automatic detection from X-ray images by utilizing deep learning methods(Elsevier Ltd, 2021) Nigam, B.; Nigam, A.; Jain, R.; Dodia, S.; Arora, N.; Annappa, B.In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients’ X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. © 2021 Elsevier Ltd