Conference TEHI, November 07-08-Samarkhand, Uzbekistan
Healthcare & Medical Imaging
Project Title:
Explainable AI for Medical Imaging, Diagnostics, and Predictive Healthcare
Overview:
This project advances AI-powered healthcare by developing interpretable, high-accuracy diagnostic systems for early disease detection, medical imaging analysis, and healthcare system optimization.
Key Research Focus Areas:
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Cancer detection (breast, brain, gallbladder) using deep learning
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Ophthalmic biomarker detection with explainable AI
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Alzheimer’s disease and neurological disorder analysis
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IoT-enabled predictive healthcare and fraud detection
Current Outcomes:
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Graph Neural Network models for cancer diagnosis
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Explainable deep learning models for clinical decision support
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AI-based healthcare fraud and risk detection systems
Funding & Collaboration Relevance:
Highly aligned with NIH, NCI, NIBIB, healthcare systems, and medical technology companies.
Published papers:
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Paper Name |
Keywords |
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A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis |
brain tumor, transfer learning, MRI, deep learning, VGG16, ResNet, XAI, medical imaging |
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OCT, ophthalmic biomarker, active learning, explainable AI, GradCAM, semi-supervised, ophthalmology |
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DeepDenseVit: A Hybrid Deep Learning Approach for Ovarian Cancer Classification |
ovarian cancer, deep learning, ViT, DenseNet, hybrid model, medical imaging, classification |