The Data Science and AI in Medical Imaging Group is a pioneering research team dedicated to advancing the field of medical imaging through the application of data science and artificial intelligence. Our mission is to develop innovative solutions that enhance diagnostic accuracy, streamline imaging processes, and ultimately improve patient outcomes. By leveraging cutting-edge machine learning algorithms, big data analytics, and state-of-the-art imaging technologies, our multidisciplinary team collaborates with clinicians, radiologists, and industry partners to translate research into real-world healthcare improvements. Our focus areas include automated image analysis, predictive modeling, and the integration of AI into clinical workflows, aiming to set new standards in medical imaging and personalized medicine.
Explore the latest publications detailing breakthroughs and insights from our researcher.
M.Azam, M. U. Hashmi et.al; “Improvement in Classification Algorithms through Model Stacking with the
Consideration of their Correlation” Int. J. Adv. Comp. Sc. & App. 10(3)(2019), 463-475
1- Performance evaluation of Optimizers for CT Scan Brain Images
2- Prediction Analysis of Breast Cancer
3- Classifiers Performance on CT Scan Images of Lung Cancer
4- Performance of Ensemble Methods on MRI Images of Brain Tumor
5- Classification of Skin Cancer Types using Deep Learning Models
6- Comparison of Conventional and Deep Learning Models Applied on CT Scans of Kidney Tumor,
Stone and Cyst
7- Findings of Best Optimizer-Classifier pair for Histopathological Images of Oral Cancer
8- Automated Lung Segmentation with Machine Learning in Medical Imaging
9- Machine Learning-Based Medical Image Segmentation for Brain Tumor Detection
10- Medical image segmentation for skin lesions using machine learning
11- Disease prediction of diabetes using machine learning: a comprehensive study and analysis
12- Cardiovascular Disease Risk Stratification and Prediction Using Machine Learning
13- Enhanced Brain Tumor Detection Through Advanced Multimodal Image Fusion
Techniques
14- A Comparison of Machine Learning Classifiers and Uncertainty Quantification Techniques for
Predicting Heart Disease
15- Comparison of Optimizer Performance for Computed Tomography Brain Images
16- GIS Overview of Public School of District Lahore using Spatial Mathematics and AI
17- Enhancing Cardiac Care: The Future of Heart Disease Prediction with Machine Learning
18- Prediction of Kidney Stone and Cancer Using ML
19- Uncertainty Quantification of Lungs Cancer using Machine Learning
20- Study of Pakistan Sign Language using Machine Learning and AI
21- Prediction of HIV/AIDS using Machine Learning and Deep Learning
22- Study of Environmental Changes using Machine Learning and AI
23- Study of Heart Disease using Machine Learning and AI
24- Uncertainty quantification of Brain Tumor CT image
25- Prediction of Money Pox Disease using Machine Learning
26- A Semantic Fake News Detection System Using Machine Learning Classifier
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