Ph.D. Researcher · Universiti Malaya

Building the Future of AI in Healthcare.

I'm Hasanul Bannah — I develop deep learning for digital pathology, explainable clinical AI, and quantum machine learning for drug discovery. Turning complex biomedical data into transparent, deployable intelligence.

Explore my research → ↓ Download CV
✉ GS in iD
Hasanul Bannah
Digital Pathology
Quantum ML
Drug Discovery
Digital PathologyDeep LearningExplainable AIQuantum Machine LearningBiomedical ImagingClinical LLMsDrug DiscoveryComputer Vision Digital PathologyDeep LearningExplainable AIQuantum Machine LearningBiomedical ImagingClinical LLMsDrug DiscoveryComputer Vision
0Publications
0First-author papers
0Innovation medals
0Citations
About

Researcher at the edge of
AI & medicine

I'm a Ph.D. researcher and Graduate Research Assistant at the Institute of Biological Sciences, Faculty of Science, Universiti Malaya — ranked among the world's top 56 universities.

My work sits where artificial intelligence meets healthcare. During my Master's at Multimedia University I built deep-learning pipelines for breast cancer histopathology — collecting IHC glass slides from UMMC, digitizing them into whole-slide images, and automating nuclei segmentation, classification and Allred scoring, all made interpretable with explainable AI.

Today my doctoral research advances quantum machine learning for drug repurposing in Alzheimer's disease — designing hybrid quantum-classical models that are efficient, robust, and clinically meaningful. I care about building tools clinicians can actually trust.

Education

Jun 2026 — Present
Ph.D., Faculty of Science
Universiti Malaya
Quantum Machine Learning for Drug Repurposing in Alzheimer's Disease
Dec 2024 — May 2026
M.Eng.Sc. (By Research)
Multimedia University · Completed
AI-Driven Nuclei Segmentation & Scoring in Breast Cancer IHC
What I do

Research focus

Four threads, one mission — intelligent, interpretable systems for medicine.

🔬

Digital Pathology

Deep learning on whole-slide images — nuclei segmentation, classification and Allred/IHC scoring for ER- and PR-stained breast cancer histopathology.

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Explainable Clinical AI

Grad-CAM, SHAP and LIME for transparent diagnostics, plus clinical NLP and large language models for real-world decision support.

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Quantum Machine Learning

Hybrid quantum-classical graph neural networks and variational circuits for drug–target interaction and Alzheimer's drug repurposing.

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Drug Discovery

AI and quantum methods for drug–target interaction prediction and repurposing — accelerating the search for Alzheimer's disease therapeutics.

Selected work

Publications

17 published & accepted works. Full list on Google Scholar. ★ = first author.

2026
★ AI-Driven Breast Cancer Nuclei Segmentation, Classification & Scoring in PR-IHC ImagesBannah, H., Ahmad Fauzi, M. F., Mansor, S., Nabi, M. S., et al.
Diagnostics · Q1 · IF 3.8DOI ↗
2026
★ PR-IHC-40X: Progesterone Receptor Immunohistochemistry Dataset for Breast Cancer DiagnosisBannah, H., Ahmad Fauzi, M. F., Mansor, S., Nabi, M. S., et al.
Data (MDPI) · Accepted
2026
Machine Learning for Chemotherapy Decision-Making in Breast Cancer Using LLMNabi, M. S., Yuden, D., … Bannah, H., et al.
Front. Digital Health · Q1 · IF 5.2DOI ↗
2026
Explainable AI in Kidney Stone Detection and Segmentation: A Mini ReviewHossen, M. J., Haque, B. M. T., Bannah, H., et al.
Front. Digital Health · Q1 · IF 5.2DOI ↗
2026
KDH-Net: Explainable Medical AI for Multiclass Kidney Disease Characterization from CT ImagesNabi, M. S., Tun, S. W., … Bannah, H., et al.
J. Clinical Medicine · Q1 · IF 3.3DOI ↗
2026
Early Detection of Chronic Kidney Disease Using Deep Learning: A Mini ReviewHossen, M. J., Bannah, H., & Sadib, R. J.
Front. Digital Health · Q1 · IF 5.2DOI ↗
2025
Federated AI-OCPP Framework for Secure & Scalable EV Charging in Smart CitiesHossen, M. S., Sarker, M. T., Nabi, M. S., Bannah, H., et al.
Urban Science · Q1 · IF 2.9DOI ↗
2026
Representational Efficiency & Noise Robustness in Hybrid Quantum-Classical Graph Neural NetworksSanto, I. A., Bannah, H., Nabi, M. S., & Abdul Karim, H. B.
Discover Quantum Science · Springer · Accepted
2026
★ A Hybrid Quantum-Classical Approach for Drug–Target Interaction Prediction in Alzheimer's DiseaseBannah, H., et al.
InCoB / ISCB-APAC 2026 · Accepted
2025
★ Brain-Controlled Energy Management in Renewable-Powered Smart HomesBannah, H., Hossen, M. S., Nabi, M. S., Karim, H. A., et al.
IEEE ETFG 2025 · Wollongong · Accepted
2025
★ Real-Time Nuclei Classification and Allred Scoring in PR-IHC Stained Breast Cancer ImagesBannah, H., et al.
IEEE InCIT 2025 · PhuketDOI ↗
2025
★ Automated Nuclei Segmentation in PR-IHC Breast Cancer Images Using CellposeBannah, H., et al.
IEEE TENCON 2025 · Kota KinabaluDOI ↗
2025
★ Breast Cancer Nuclei Segmentation in ER-IHC Images Using Deep LearningBannah, H., Ahmad Fauzi, M. F., Binti Mansor, S., et al.
IEEE MECON 2025 · CyberjayaDOI ↗
2025
Travelling Salesman Problem in the Classical to Quantum Era: A Comparative ReviewSanto, I. A., Lim, S. C., … Bannah, H., & Kabir, F.
IEEE InCIT 2025 · PhuketDOI ↗
2025
Adaptive Learning for Elementary Students Using AI to Support Individual Learning StylesNabi, M. S., Islam, S., … Bannah, H., et al.
IEEE MECON 2025DOI ↗
2024
Advancements in Multi-View Human Activity Recognition for Ambient Assisted LivingBari, A., … Bannah, H.
IEEE MECON 2024DOI ↗
2024
Lightweight Deep Learning for Human Activity Recognition in Ambient Assisted LivingBari, A., … Bannah, H.
IEEE MECON 2024DOI ↗
Recognition

Awards & funding

🥇
2026

Gold Medal — iNVENTX

Hybrid Quantum-Classical Graph Neural Networks.

🥈
2026

Silver Medal — iNVENTX

Decoding Human Brain Signals for Smart Living using AI.

🏆
2025

Gold & Best of the Best — DIFCON

Deep-learning nuclei segmentation in ER-IHC images.

🥇
2024

Gold Medal — iNVENTX (Poster)

AI-driven breast cancer nuclei segmentation.

✈️
2025–26

Travel Grants & Full Funding

InCoB/ISCB-APAC, InCIT (Phuket) & IEEE TENCON (Sabah).

🎓
2026

Fully-Funded GRA — Universiti Malaya

Stipend + tuition-fee waiver for Ph.D. research.

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Let's connect

Open to collaborations & Ph.D. opportunities

In medical AI, computational pathology, biomedical imaging and quantum machine learning.

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