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Academic Experts
Dr. Tarun Agrawal

Biography

Dr. Tarun Agrawal has done the Ph.D. at Department of Computer Science & Engineering from National Institute of Technology Hamirpur, HP, India. He did his B. Tech and M. Tech from the Zakir Hussain College of Engineering & Technology, Aligarh Muslim University, in 2014 and 2016, respectively. His research interests are Computer Vision, Medical Imaging, Machine and Deep Learning. He has published several SCIE/Scopus journal and International Conference papers in medical image analysis field.

Research Highlights

Dr. Tarun Agrawal’s research contributions lie primarily in the field of medical image analysis using deep learning and computer vision. His publications include the development of efficient and robust deep learning models for lung segmentation, COVID-19 lesion detection, and pneumothorax classification in chest X-ray and CT imaging to improve segmentation accuracy. He has also worked on brain tumour classification through novel approaches and addressing challenges such as class imbalance and multi-scale feature learning. Beyond disease detection, his studies extend to fetal ultrasound classification from ECG images, showcasing the interdisciplinary application of AI in healthcare. In addition, Dr. Agrawal has authored comprehensive survey and review articles, including a detailed review of the Bat Algorithm and systematic studies on deep learning approaches for medical image segmentation and classification. Collectively, his publication record highlights a strong focus on designing lightweight, accurate, and application-driven AI models for medical imaging, bridging methodological innovation with practical clinical needs.

Areas of Interest
  • 1. Medical Image Analysis
  • 2. Computer Vision
  • 3. Deep Learning
Publications
  1. Tarun Agrawal, and Prakash Choudhary. "FocusCovid: Automated COVID-19 detection using deep learning with chest X-ray images." Evolving Systems (2022): 1-15. (IF = 3.2)
  2. Tarun Agrawal, and Vijay Kumar. "A Systematic Review on Bat Algorithm: Theoretical Foundation, Variants, and Applications." Archives of Computational Methods in Engineering (2021): 1-30. (IF = 9.7)
  3. Tarun Agrawal, and Prakash Choudhary. "Segmentation and classification on chest radiography: a systematic survey." The Visual Computer (2022): 1-39. (IF = 3.5)
  4. Tarun Agrawal, and Prakash Choudhary. “EfficientUNet: Modified Encoder-Decoder architecture for the Lung Segmentation in Chest X-ray Images.” Expert Systems (2022): 1-19. (IF = 3.3)
  5. Tarun Agrawal, and Prakash Choudhary. “ALCNN: Attention based lightweight convolutional neural network for pneumothorax detection in chest x-rays” Biomedical Signal Processing and Control (2023): 1746-8094 (IF = 5.1)
  6. Tarun Agrawal, and Prakash Choudhary. "ReSE‐Net: Enhanced UNet architecture for lung segmentation in chest radiography images." Computational Intelligence (2023). (IF = 2.8)
  7. Tarun Agrawal, and Prakash Choudhary. “Covid-SegNet: Encoder-decoder based architecture for covid-19 lesion segmentation in chest X-ray”. Multimedia System (2023). (IF = 3.9)
  8. Prashant, Kunwar, Prakash Choudhary, Tarun Agrawal, and Evam Kaushik. "OWAE-Net: Covid-19 detection from ECG images using Deep Learning and Optimized Weighted Average Ensemble Technique." Intelligent Systems with Applications (2022): 200154.(Cite Score = 4.6)
  9. Tarun Agrawal, Prakash Choudhary, Achyut Shankar, Prabhishek Singh, and Manoj Diwakar. "MultiFeNet: Multi‐scale feature scaling in deep neural network for the brain tumour classification in MRI images." International Journal of Imaging Systems and Technology (2024). (IF = 3.3)
  10. Tarun Agrawal, Prakash Choudhary, Vijay Kumar, Prabhishek Singh, Manoj Diwakar and Sandeep Kumar. "A comparative study of brain tumor classification on unbalanced dataset using deep neural networks." Biomedical Signal Processing and Control 94 (2024): 106256. (IF = 5.1)
  11. Sourav, Prakash Choudhary, Priyanka, Tarun Agrawal. “HCN: Hybrid capsule network for fetal plane classification in ultrasound images.” International Journal of Imaging Systems and Technology (2024). (IF = 3.3) (Accepted)