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Your search keyword '"Gour, Mahesh"' showing total 17 results
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17 results on '"Gour, Mahesh"'

2. Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images

Author

Gour, Mahesh and Jain, Sweta

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Automatic and rapid screening of COVID-19 from the chest X-ray images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide in 2020. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in X-ray images. In this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray images. We obtain different sub-models from the VGG19 and developed a 30-layered CNN model (named as CovNet30) during the training, and obtained sub-models are stacked together using logistic regression. The proposed CNN model combines the discriminating power of the different CNN`s sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes. In addition, we generate X-ray images dataset referred to as COVID19CXr, which includes 2764 chest x-ray images of 1768 patients from the three publicly available data repositories. The proposed stacked CNN achieves an accuracy of 92.74%, the sensitivity of 93.33%, PPV of 92.13%, and F1-score of 0.93 for the classification of X-ray images. Our proposed approach shows its superiority over the existing methods for the diagnosis of the COVID-19 from the X-ray images., Comment: 6 tables, 4 figures

Published
2020

5. DeepRNNetSeg: Deep Residual Neural Network for Nuclei Segmentation on Breast Cancer Histopathological Images

Author

Gour, Mahesh, Jain, Sweta, Agrawal, Raghav, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nain, Neeta, editor, Vipparthi, Santosh Kumar, editor, and Raman, Balasubramanian, editor

Published
2020
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7. Robust nuclei segmentation with encoder‐decoder network from the histopathological images

Author

Gour, Mahesh, Jain, Sweta, Telegam Setti, Sunilkumar, Gour, Mahesh, Jain, Sweta, and Telegam Setti, Sunilkumar

Abstract

uclei segmentation is a prerequisite and an essential step in cancer detection and prognosis. Automatic nuclei segmentation from the histopathological images is challenging due to nuclear overlap, disease types, chromatic stain variability, and cytoplasmic morphology differences. Furthermore, it is demanding to develop a single accurate method for segmenting nuclei of different organs because of the diversity in nuclei size, shape, and appearance across the various organs. To address these challenges, we developed a robust Encoder-Decoder network for nuclei segmentation from the multi-organ histopathological images. In this approach, we utilize a pre-trained EfficientNet-B4 as an Encoder subnetwork and design a new Decoder subnetwork architecture. Additionally, we have applied morphological operation-based post-processing to improve the segmentation results. The performance of our approach has been evaluated on three public datasets, namely, Kumar, TNBC, and CPM-17 datasets, which contain histopathological images of seven organs, one organ, and four organs, respectively. The proposed method achieved an aggregated Jacquard index of 0.636, 0.611, and 0.706 on Kumar, TNBC, and CPM-17 datasets, respectively. Our proposed approach also shows superiority over the existing methods.

Published
2024
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9. Pulmonary Lung Nodule Detection from Computed Tomography Images Using Two-Stage Convolutional Neural Network.

Author

Jain, Sweta, Choudhari, Pruthviraj, and Gour, Mahesh

Subjects
CONVOLUTIONAL neural networks, COMPUTED tomography, PULMONARY nodules, LUNGS, IMAGE databases, LUNG cancer
Abstract

Lung cancer is one of the leading causes of cancer-related death in people all over the world. Lung cancer screening is a crucial part of the diagnosis of cancer. The initial sign of lung cancer is the pulmonary nodules that can be detected based on the computed tomography (CT) scan images. In some cases, the nodules are not obvious and may take a trained eye and a considerable amount of time to detect. The automatic detection of the nodules can save considerable time and money, thus opening prescreening accessibility, ultimately saving lives. Hence, a two-stage convolutional neural network (CNN) model is proposed to segment and detect pulmonary lung nodules from CT scan images. In the first stage, we have used U-net for nodule segmentation, and in the second stage, a CNN model is designed to reduce the false positives (FPs) generated in the previous stage, which enhances the overall efficiency and correctness of the system. The proposed method's performance is evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative dataset. The proposed two-stage CNN model achieved an accuracy of 84.4 %, where the FP per true positive is reduced from 11.1 to 0.97. The proposed model shows its superiority over the existing methods for nodule detection. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Cross-Eyed 2017 : Cross-Spectral Iris/Periocular Recognition Competition

Author

Sequeira, Ana F., Chen, Lulu, Ferryman, James, Wild, Peter, Alonso-Fernandez, Fernando, Bigun, Josef, Raja, Kiran B., Raghavendra, R., Busch, Christoph, Freitas Pereira, Tiago, Marcel, Sébastien, Sangeeta Behera, Sushree, Gour, Mahesh, Kanhangad, Vivek, Sequeira, Ana F., Chen, Lulu, Ferryman, James, Wild, Peter, Alonso-Fernandez, Fernando, Bigun, Josef, Raja, Kiran B., Raghavendra, R., Busch, Christoph, Freitas Pereira, Tiago, Marcel, Sébastien, Sangeeta Behera, Sushree, Gour, Mahesh, and Kanhangad, Vivek

Abstract

This work presents the 2nd Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward. © 2017 IEEE, SIDUS-AIR, FastPass, PROTECT, HFACE, SWAN

Published
2017
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16. Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

Author

Sequeira, Ana F., primary, Chen, Lulu, additional, Ferryman, James, additional, Wild, Peter, additional, Alonso-Fernandez, Fernando, additional, Bigun, Josef, additional, Raja, Kiran B., additional, Raghavendra, R., additional, Busch, Christoph, additional, de Freitas Pereira, Tiago, additional, Marcel, Sebastien, additional, Behera, Sushree Sangeeta, additional, Gour, Mahesh, additional, and Kanhangad, Vivek, additional

Published
2017
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17. Automated COVID-19 Detection from X-ray and CT Images with Stacked Ensemble Convolutional Neural Network

Author

Gour, Mahesh and Jain, Sweta

Abstract

Automatic and rapid screening of COVID-19 from the radiological (X-ray or CT scan) images has become an urgent ion of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is challenging due to the discrepancy between the radiological images of COVID-19 and other viral pneumonia. So, in this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray and CT images. In the proposed approach, different sub-models have been obtained from the VGG19 and the Xception models during the training. Thereafter, obtained sub-models are stacked together using softmax classifier. The proposed stacked CNN model combines the discriminating power of the different CNN’s sub-models and detects COVID-19 from the radiological images. In addition, we collect CT images to build a CT image dataset and also generate an X-ray images dataset by combining X-ray images from the three publicly available data repositories. The proposed stacked CNN model achieves a sensitivity of 97.62% for the multi-class classification of X-ray images into COVID-19, Normal and Pneumonia Classes and 98.31% sensitivity for binary classification of CT images into COVID-19 and no-Finding classes. Our proposed approach shows superiority over the existing methods for the detection of the COVID-19 cases from the X-ray radiological images.

Published
2021
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