Ankit Kandpal
Punjab Engineering College (Deemed to be University), University of Mumbai
0000-0003-4339-7047
About
Dedicated PhD student at the prestigious Centre for Biomedical Engineering, Indian Institute of Technology Delhi, pioneering research at the intersection of artificial intelligence and neurology. My work focuses on developing AI algorithms for the analysis of brain tumors and stroke using both MRI and CT imaging modalities.
With a strong foundation in electronics engineering, holding a BTech from the University of Mumbai and an MTech from Punjab Engineering College, Chandigarh, I bring a unique perspective to the field of biomedical engineering. My interdisciplinary background allows me to bridge the gap between traditional engineering principles and advanced medical imaging techniques.
Research Focus:
1. Applying state-of-the-art deep learning models to enhance brain tumor detection and classification
2. Developing AI-driven solutions for early stroke diagnosis and prognosis prediction
3. Optimizing image processing techniques for improved MRI and CT data analysis
Areas of Expertise:
1. Artificial Intelligence in Healthcare
2. Deep Learning and Neural Networks
3. Medical Image Analysis and Processing
4. Brain Tumor and Stroke Research
5. Signal and Image Processing
Passionate about leveraging technology to improve patient outcomes, I am constantly exploring innovative ways to apply AI in biomedical engineering. My goal is to contribute to the development of intelligent diagnostic tools that can assist healthcare professionals in making faster, more accurate decisions.
Employment
Employment history is unavailable.
Education
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Punjab Engineering College (Deemed to be University) M.Tech.2018 - 2020
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University of Mumbai B.E.2013 - 2017
Projects & Funding
Projects & funding information is unavailable.
Publications (7)
- Isolated intracranial fungal mass lesions in immunocompetent subjects mimicking glial neoplasms on magnetic resonance imaging – A case series Save
- Segmentation and classification of brain tumors using artificial intelligence and multiparametric MRI Save
- Automatic Multiclass Tissue Segmentation Using Deep Learning in Brain MR Images of Tumor Patients Save
- Ischemic Stroke Lesion Segmentation on Multiparametric CT Perfusion Maps Using Deep Neural Network Save
- Automatic pipeline for segmentation of LV myocardium on quantitative MR T1 maps using deep learning model and computation of radial T1 and ECV values Save
- Automated Brain Extraction in Multi-Sequence MR Images Across Multiple Centres and Pathologies Save
- Retinal Image Enhancement Using Edge-based Texture Histogram Equalization Save