Bridging Academia & Industry

Translating cutting-edge AI/ML research into production-ready systems that deliver measurable impact across healthcare and industrial sectors. Research focused on medical imaging, predictive maintenance, and causal inference.

Publications

Under Review (Elsevier)

Model Accuracy

93.2%

Multi-Class Classification

Research Role

First Author

DHNN Study

Datasets

MRI

Large-Scale Preprocessing Pipeline

Under Review

Multi-Class Brain Tumor Classification and Grade Estimation using Dual-Head Neural Network

Elsevier Results in Engineering (Manuscript No. RINENG-D-26-00182)First Author2026

Proposed a novel Dual-Head Neural Network leveraging EfficientNetB0 for simultaneous multi-class brain tumor classification and grade prediction from MRI scans.

Key Findings

›Achieved 93.2% multi-class classification accuracy
›Simultaneous tumor type and grade prediction in one architecture
›Large-scale MRI preprocessing with cleaning, labeling, and augmentation
›Designed for practical medical AI deployment pathways

Methodology

›EfficientNetB0 backbone with transfer learning
›Dual-head architecture for multi-task learning
›MRI preprocessing pipeline using OpenCV and Pandas
›Rigorous training/validation for multi-class classification settings

Impact

Demonstrates a robust and efficient workflow for clinically relevant tumor typing and grading support in medical imaging contexts.

TensorFlowKerasPythonOpenCVMedical AI

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Open to research collaborations, joint publications, and consulting opportunities in AI/ML, medical imaging, and industrial systems.

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