Dylan Jayabahu

Research Publications

Conference Paper

2025

Deep Learning Model for Invasive Ductal Carcinoma Detection in Histopathology Images

Dylan Jayabahu (sole author)

IEEE CCECE 2025 IEEE Xplore

Introduces a novel computer-aided diagnosis (CAD) system for detecting Invasive Ductal Carcinoma (IDC) in histopathology images using advanced deep learning techniques. The system incorporates innovative sliding window-based heatmaps and a unique oversampling technique that extracts patches from homogeneous regions in whole-slide images, addressing class imbalance while maintaining biological integrity. Achieved state-of-the-art performance with 89.06% balanced accuracy and 86.68% F1-score, outperforming existing models in the literature.

                            Innovation:
                            Novel sliding window heatmaps and homogeneous region
                                oversampling
                        

                            Performance:
                            89.06% balanced accuracy, 86.68% F1-score (SOTA)
                        

                            Venue:
                            IEEE Canadian Conference on Electrical and Computer
                                Engineering (CCECE)
                        

                            Location:
                            Vancouver, Canada (May 2025)
                        

                            Impact:
                            Clinical tool for pathologists with interpretable heatmap
                                visualization
                        

IEEE Xplore TechRxiv CCECE 2025

Journal Article

2024

Dataset for Real-World Human Action Detection Using FMCW mmWave Radar

Dylan Jayabahu, Parthipan Siva

CVIS 2024 JCVIS

Presents a real-world mmWave radar dataset for human action detection with applications in healthcare and home automation. Unlike existing research limited to controlled environments, this work captures natural activities across 28 residences, focusing on privacy-preserving sensing technology for aging-in-place monitoring. Developed and evaluated CNN-based models for action detection, demonstrating the challenges and potential of real-world radar-based human activity recognition systems.

                            Dataset:
                            28 homes, 458 sit-down and 454 stand-up actions
                        

                            Technology:
                            TI IWR6843AOP FMCW mmWave sensor with 3D point clouds
                        

                            Model:
                            3-layer CNN with sliding window detection approach
                        

                            Presentation:
                            Presented at CVIS 2024, University of Waterloo
                        

                            Innovation:
                            First real-world dataset vs. controlled simulations
                        

JCVIS arXiv CVIS 2024

Professional Certificates

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IBM Generative ai and large language models architecture and data preparation

IBM

Gen AI Foundational Models for NLP & Language Understanding

Achieved: September 2025

NLP Pytorch RNN Word2Vec Seq2Seq

Dylan Jayabahu

Resume

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Research Publications

Deep Learning Model for Invasive Ductal Carcinoma Detection in Histopathology Images

Dataset for Real-World Human Action Detection Using FMCW mmWave Radar

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