📝 Research Biography
I am a professor in the Department of Computer Science & Engineering at MM Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India. My work covers artificial intelligence, machine learning, deep learning, and medical image processing, with a focus on cancer diagnosis and prognosis. I have spent more than 27 years in teaching, research, and academic leadership.
I hold a B. Tech. (2009) and an M. Tech. (2012) in Computer Science & Engineering, a UGC-NET qualification in Computer Science (2012), and a PhD in Computer Science & Engineering from I.K. Gujral Punjab Technical University (2023). My doctoral thesis was on diagnosing lung cancer with data mining techniques. In 2025, I completed the Diploma in AI Essentials for Business at Harvard Business School Online. I am currently a postdoctoral fellow at Lincoln University College, Malaysia, where I am developing a unified, explainable, multi-stage deep learning framework to predict lung malignancy from DICOM images.
On the technical side, I build, train and evaluate deep learning models for imaging and clinical risk prediction. I work with CNN architectures such as ResNet-50/101, EfficientNet-B3, DenseNet201, GoogLeNet, AlexNet and SqueezeNet, along with feature-fusion and ensemble methods, generative models (PGANs) and LSTM-based sequence models. I use LIME and SHAP for explainability. My toolset includes Python (TensorFlow, Keras, PyTorch, Scikit-learn), MATLAB, R, OpenCV, SimpleITK and cloud platforms including Google Cloud, AWS and Oracle Cloud. My applied work also covers NLP and sentiment analysis, mental-health text analysis, AI-enabled IoT, and wearable health monitoring.
My research is also personal. I have been a lung cancer survivor for more than three decades, and that experience shapes my interest in early detection, survivorship, and the risk of secondary primary cancers. The Indian newspaper The Tribune has featured this work, and I wrote about my own experience in A Journey of Healing. I am an active member of the International Association for the Study of Lung Cancer (IASLC) and the European Association for Cancer Research (EACR), and I have presented at IASLC conferences in Toronto, Vienna, Singapore and San Diego.
My scholarly output includes about 30 SCI- and Scopus-indexed journal articles, nearly 30 peer-reviewed conference papers (IEEE and Springer LNNS), 13 or more book chapters published or accepted (Springer, Wiley, Taylor & Francis, CRC Press, Bentham), four authored books, and six published patent applications. The patents cover IoT wearables, a smart asthma inhaler, an AI-based agricultural robot, and a deep learning ensemble for early lung disease detection. I serve on the editorial boards of three journals and review for Scientific Reports, Pattern Recognition, Pattern Recognition Letters and Computers and Electrical Engineering, among others. I am also an Elsevier Certified Peer Reviewer.
I have taught at Kurukshetra University, Sachdeva Institute of Technology, NIMS University, Panipat Institute of Engineering and Technology, Chandigarh University (where I was Research Coordinator) and now at Maharishi Markandeshwar Deemed University. My courses span machine learning and deep learning, artificial intelligence, computer vision, medical image processing, soft computing, computer networks and research methodology. I have supervised more than 20 postgraduate dissertations, and I currently supervise three PhD scholars in AI-based medical imaging.
Unified Deep Learning Models for Enhanced Lung Cancer Prediction with ResNet-50–101 and EfficientNet-B3 using DICOM Images
Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyse and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying squamous cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.
🔗 https://doi.org/10.1186/s12880-024-01241-4
Multi-Model Machine Learning Framework for Lung Cancer Risk Prediction: A Comparative Analysis of Nine Classifiers with Hybrid and Ensemble Approaches
LC continues to be the most prevalent cause of cancer deaths worldwide, which calls for sophisticated detection strategies. The present study investigates 34 demographic, behavioural, and haematological risk factors based on a sample of 2,000 patient data records. A multi-model machine learning approach compares nine algorithms: KNN, AdaBoost (AB), logistic regression (LR), random forest (RF), SVM, naive Bayes (NB), decision tree (DT), gradient boosting (GB), and stochastic gradient descent (SGD). Performance measures (accuracy, sensitivity, specificity, F1-score, AUC) identify quantitative differences: GB had the best F1-scores (0.953) and NB had the second-best F1-score (0.945), while GB had the best sensitivity (99.1%). The KNN-AB hybrid model reported the highest accuracy with 99.5%, while RF reported the highest AUC with a value of 0.92. Ensemble approaches (RF, GB) showed robust predictive performance across measures through integration of complementary strengths of base models. Lasso and ridge regression were able to minimise overfitting, making them easier to interpret. Therapeutic uses include integration into electronic health records (EHRs) for computerised risk stratification, early LC screening, and public health interventions in high-risk subjects (smokers with abnormal haematologic markers). The research highlights the value of hybrid ML models to integrate behavioural and biological data to effectively predict LC. Subsequent work can expand predictive capabilities through imaging data and genomics data incorporation and continue to advance early identification and patient-specific therapy options. This is an intersection of computational advances and clinical translation, providing scalable solutions for global LC diagnosis.
🔗 https://doi.org/10.1016/j.slast.2025.100314
