Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Dutta, Maitreyee
- Tracking of Hand Movements in Gesture Recognition a Review on Techniques and Approaches
Abstract Views :128 |
PDF Views:0
Authors
Affiliations
1 Computer Science and Engineering, NITTTR, Chandigarh, IN
2 Electronics and Communications, UIET, Chandigarh, IN
1 Computer Science and Engineering, NITTTR, Chandigarh, IN
2 Electronics and Communications, UIET, Chandigarh, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 11 (2014), Pagination: 57-60Abstract
Gesture recognition is a vast topic of research in computer science. It deals with creating a system that can identify human gestures and use them for different applications. In gesture recognition, a camera is used to capture human body's motions which are sent to the computer for interpreting human body language. One of the major application areas of gesture recognition is automatic sign language recognition. In sign language motion of human body parts like hands and head are very important to consider. Whenever trajectories and movements are considered, tracking plays a vital role and is essential too. Tracking helps in recognizing the motion trajectories. This paper presents literature review on research and findings by researchers on different techniques and approaches of tracking.Keywords
Review, Tracking, Sign Language, Mean shift, Kalman Filter, Condensation, Particle Filter.Full Text
- Comparative Analysis of Different Deep Learning Models for COVID-19 Detection Using X-Ray Images
Abstract Views :144 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, NITTTR Chandigarh, 160019, IN
2 Professor & Head, IMEE, NITTTR Chandigarh, 160019, IN
3 Associate Professor, NITTTR Chandigarh, 160019, IN
1 Research Scholar, NITTTR Chandigarh, 160019, IN
2 Professor & Head, IMEE, NITTTR Chandigarh, 160019, IN
3 Associate Professor, NITTTR Chandigarh, 160019, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 192-211Abstract
Coronavirus disease (COVID-19), one of the most contagious diseases of the twenty-first century was firstly discovered in Wuhan, China, in December 2019 and has spread aggressively around the globe. It is becoming difficult for the medical staff to quickly detect the ailment and prevent it from spreading due to its rapid spread and rising numbers. As a result, automating the diagnostic process has become necessary. One of the emerging study fields that can more accurately address this issue is medical image analysis and a chest X-ray scan is an efficient screening method for identifying COVID-19 cases by training deep learning models. This paper proposes a neural network for classifying COVID-19 cases from other cases (normal, pneumonia, and lung opacity) that has been pre-trained on a labeled chest X-ray dataset and compares the results with other CNN models (VGG16, VGG19, NASNetLarge, MobileNetV2, InceptionResNetV2, DenseNet121, ResNet50). The models were tested against a publicly accessible chest X-ray dataset in which data augmentation and under-sampling techniques were used to eliminate the problems of data scarcity and data imbalance and found that the suggested model performed best, with an accuracy of 93.70%.Keywords
COVID-19, Chest X-Ray, Medical Imaging, Deep Learning, Classification, Convolutional Neural Network, Transfer Learning.Full Text
References
- F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, K. He, Y. Shi, and D. Shen, “Review of Artificial Intelligence Techniques in imaging data acquisition, segmentation, and diagnosis for covid-19,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 4–15, 2021.
- D. Dong, Z. Tang, S. Wang, H. Hui, L. Gong, Y. Lu, Z. Xue, H. Liao, F. Chen, F. Yang, R. Jin, K. Wang, Z. Liu, J. Wei, W. Mu, H. Zhang, J. Jiang, J. Tian, and H. Li, “The role of imaging in the detection and management of covid-19: A Review,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 16–29, 2021.
- Y. Oh, S. Park, and J. C. Ye, “Deep learning covid-19 features on CXR using limited training data sets,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2688–2700, 2020.
- J. P. Kanne, B. P. Little, J. H. Chung, B. M. Elicker, and L. H. Ketai, “Essentials for radiologists on covid-19: An update—radiology scientific expert panel,” Radiology, vol. 296, no. 2, 2020.
- M. M. Islam, F. Karray, R. Alhajj, and J. Zeng, “A review on Deep Learning techniques for the diagnosis of novel coronavirus (COVID-19),” IEEE Access, vol. 9, pp. 30551–30572, 2021.
- M. Canayaz, “MH-COVIDNet: Diagnosis of COVID-19 using Deep Neural Networks and meta-heuristic-based feature selection on X-ray images,” Biomedical Signal Processing and Control, vol. 64, pp. 1-12, 2021.
- J. Zhang, Y. Xie, G. Pang, Z. Liao, J. Verjans, W. Li, Z. Sun, J. He, Y. Li, C. Shen, and Y. Xia, “Viral pneumonia screening on chest x-rays using confidence-aware anomaly detection,” IEEE Transactions on Medical Imaging, vol. 40, no. 3, pp. 879–890, 2021.
- H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN Architectures, dataset characteristics and transfer learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, 2016.
- G. Litjens, T. Kooi, B. E. Bejnordi, A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on Deep Learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.
- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
- D. R. Nayak, R. Dash, B. Majhi, R. B. Pachori, and Y. Zhang, “A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer,” Biomedical Signal Processing and Control, vol. 58, pp. 1-11, 2020.
- A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with Deep Neural Networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.
- X. Gu, L. Pan, H. Liang, and R. Yang, “Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography,” Proceedings of the 3rd International Conference on Multimedia and Image Processing, pp.88-93, 2018.
- V. Chouhan, S. K. Singh, A. Khamparia, D. Gupta, P. Tiwari, C. Moreira, R. Damaševičius, and V. H. de Albuquerque, “A novel transfer learning based approach for pneumonia detection in chest X-ray images,” Applied Sciences, vol. 10, no. 2, pp. 1-17, 2020.
- P. Lakhani and B. Sundaram, “Deep learning at chest radiography: Automated Classification of pulmonary tuberculosis by using convolutional neural networks,” Radiology, vol. 284, no. 2, pp. 574–582, 2017.
- P. Rajpurkar, J. Irvin, R. L. Ball, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. P. Langlotz, B. N. Patel, K. W. Yeom, K. Shpanskaya, F. G. Blankenberg, J. Seekins, T. J. Amrhein, D. A. Mong, S. S. Halabi, E. J. Zucker, A. Y. Ng, and M. P. Lungren, “Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists,” PLOS Medicine, vol. 15, no. 11, 2018.
- S. Tabik, A. Gomez-Rios, J. L. Martin-Rodriguez, I. Sevillano-Garcia, M. Rey-Area, D. Charte, E. Guirado, J. L. Suarez, J. Luengo, M. A. Valero-Gonzalez, P. Garcia-Villanova, E. Olmedo-Sanchez, and F. Herrera, “COVIDGR dataset and Covid-SDNet methodology for predicting COVID-19 based on chest X-ray images,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 12, pp. 3595–3605, 2020.
- S. Tang, C. Wang, J. Nie, N. Kumar, Y. Zhang, Z. Xiong, and A. Barnawi, “EDL-COVID: Ensemble deep learning for COVID-19 case detection from chest X-ray images,” IEEE Transactions on Industrial Informatics, vol. 17, no. 9, pp. 6539–6549, 2021.
- X. Wang, L. Jiang, L. Li, M. Xu, X. Deng, L. Dai, X. Xu, T. Li, Y. Guo, Z. Wang, and P. L. Dragotti, “Joint learning of 3D lesion segmentation and classification for explainable COVID-19 diagnosis,” IEEE Transactions on Medical Imaging, vol. 40, no. 9, pp. 2463–2476, 2021.
- L. Sun, Z. Mo, F. Yan, L. Xia, F. Shan, Z. Ding, B. Song, W. Gao, W. Shao, F. Shi, H. Yuan, H. Jiang, D. Wu, Y. Wei, Y. Gao, H. Sui, D. Zhang, and D. Shen, “Adaptive feature selection guided Deep Forest for covid-19 classification with chest CT,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2798–2805, 2020.
- A. Shamsi, H. Asgharnezhad, S. S. Jokandan, A. Khosravi, P. M. Kebria, D. Nahavandi, S. Nahavandi, and D. Srinivasan, “An uncertainty-aware transfer learning-based framework for covid-19 diagnosis,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, pp. 1408–1417, 2021.
- M. Yamac, M. Ahishali, A. Degerli, S. Kiranyaz, M. E. Chowdhury, and M. Gabbouj, “Convolutional sparse support estimator-based COVID-19 recognition from X-ray images,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 1810–1820, 2021.
- Z. Yang, L. Zhao, S. Wu, and C. Y.-C. Chen, “Lung lesion localization of COVID-19 from chest CT image: A novel weakly supervised learning method,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 1864–1872, 2021.
- M. Gazda, J. Plavka, J. Gazda, and P. Drotar, “Self-supervised deep convolutional neural network for chest X-ray classification,” IEEE Access, vol. 9, pp. 151972–151982, 2021.
- E. F. Ohata, G. M. Bezerra, J. V. Chagas, A. V. Lira Neto, A. B. Albuquerque, V. H. Albuquerque, and P. P. Reboucas Filho, “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 1, pp. 239–248, 2021.
- J. Cheng, W. Zhao, J. Liu, X. Xie, S. Wu, L. Liu, H. Yue, J. Li, J. Wang, and J. Liu, “Automated diagnosis of COVID-19 using deep supervised Autoencoder with multi-view features from CT images,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 5, pp. 2723–2736, 2022.
- B. Xiao, Z. Yang, X. Qiu, J. Xiao, G. Wang, W. Zeng, W. Li, Y. Nian, and W. Chen, “PAM-DenseNet: A deep convolutional neural network for computer-aided COVID-19 diagnosis,” IEEE Transactions on Cybernetics, vol. 52, no. 11, pp. 12163–12174, 2022.
- M. F. Aslan, K. Sabanci, and E. Ropelewska, “A new approach to COVID-19 detection: An ANN proposal optimized through tree-seed algorithm,” Symmetry, vol. 14, no. 7, pp. 1-13, 2022.
- T. Rahman, “Covid-19 radiography database,” Kaggle, 19-Mar-2022. [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.
- M. F. Aslan, M. F. Unlersen, K. Sabanci, and A. Durdu, “CNN-based Transfer Learning–BILSTM network: A novel approach for COVID-19 infection detection,” Applied Soft Computing, vol. 98, pp. 1-12, 2021.
- M. F. Aslan, K. Sabanci, and A. Durdu, “A CNN-based novel solution for determining the survival status of heart failure patients with clinical record data: Numeric to image,” Biomedical Signal Processing and Control, vol. 68, pp. 1-8, 2021.
- L. Perez and J. Wang, “The effectiveness of data augmentation in image classification using deep learning,” arXiv preprint arXiv:1712.04621, 2017.
- Z. N. Swati, Q. Zhao, M. Kabir, F. Ali, Z. Ali, S. Ahmed, and J. Lu, “Brain Tumor Classification for MR images using transfer learning and fine-tuning,” Computerized Medical Imaging and Graphics, vol. 75, pp. 34–46, 2019.