Dr. Manoj Kumar Singh is an Assistant Professor and researcher specializing in Machine Learning, Deep Learning, and Medical Image Analysis. He earned his Ph.D. in Computer Science and Technology from Jawaharlal Nehru University (JNU), New Delhi, where his doctoral research focused on the development of advanced deep learning techniques for medical data analysis and image segmentation. He also holds an M.Tech. in Computer Science & Technology from JNU, along with M.Sc. and B.Sc. (Hons.) degrees in Mathematics from the University of Delhi.

His research focuses on developing intelligent and computationally efficient algorithms for medical image segmentation, disease diagnosis, and pattern recognition. His work integrates state-of-the-art deep learning architectures, attention mechanisms, transformer-based models, and mathematical optimization techniques to address complex challenges in healthcare and computer vision. In addition to medical image analysis, his research interests include plant disease detection, post-quantum cryptography, and explainable artificial intelligence.

Dr. Singh has published his research in leading international peer-reviewed journals and conference proceedings, with contributions spanning medical imaging, image processing, and cryptography. His work has received recognition from the research community, including the Global Distinguished Young Scientist Award, and he has qualified prestigious national examinations such as the UGC Junior Research Fellowship (JRF), GATE, and IIT-JAM.

Publications

• M. K. Singh, S. Chand, and D. Kumar, “DenseSegNet: Densely Separable Network for Cell Nuclei Segmentation with Multi-Scale Feature Fusion and Adaptive Attention Mechanisms,” International Journal of Imaging Systems and Technology, vol. 35(3), e70103, 2025. https://doi.org/10.1002/ima.70103
• M. K. Singh, S. Chand, and D. Kumar, “CRCNet: Convolutional Multi-Layer Perceptron Encoder with Attention Module for Colorectal Cancer Segmentation,” Evolving Systems, vol. 16, no. 84, 2025. https://doi.org/10.1007/s12530-025-09716-7
• K. Sahu, M. K. Singh, M. Niranjan, and P. Joshi, “HACDRAN: A Novel Hybrid Atrous Convolution Model with Densenet and Residual Attenuation Network for Multi-Plant Leaf Disease Classification,” Transactions on Emerging Telecommunications Technologies, vol. 36, no. 1, e70046, 2025. https://doi.org/10.1002/ett.70046
• K. Sahu, S. Tiwari, M. K. Singh, et al., “Plant Disease Detection Using a Hybrid Dilated CNN with Attention Mechanisms and Optimized Mask R-CNN Segmentation,” Scientific Reports, 2025. https://doi.org/10.1038/s41598-025-26192-w
• M. K. Singh and S. Chand, “Hybrid sigmoid activation function and transfer learning assisted breast cancer classification on histopathological images,” Multimedia Tools and Applications, vol. 83, no. 20, pp. 59 043–59 060, 2024. https://doi.org/10.1007/s11042-023-17808-2