Graduate Researcher • Bio-Computing Research Center, HITSZ • CSC Scholar

Md Abdul Mutalib

Harbin Institute of Technology, Shenzhen (HITSZ) • Bio-Computing Research Center

I research Computer Vision, 3D Vision, Medical Image Analysis, and Deep Learning Architectures at the Bio-Computing Research Center, HITSZ. My current work explores Semantic-Temporal 3D Gaussian Splatting for surgical anatomy understanding, alongside noise-resilient diagnostic pipelines for neuroimaging and chest radiography.

Lead Research ↓All Publications (6)Curriculum Vitae Contact

Lead Research Publication

★ Featured First-Author Study

Conference Paper•2026•First Author•2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)

Two-Stage Noise-Reduced Pneumonia Detection in Chest X-Ray Images Using Denoising Autoencoder–CNN Integration

Md Abdul Mutalib, Kopil Das, Mohammad Hossain, Shahriar Hussain, Avijit Deb Nath, Md Mahmud Zaman, Md. Alamgir Hossain

Core Research Question & Key Result:We tested whether removing image noise before classification helped a model identify pneumonia in chest X-rays. On the study dataset, accuracy rose from 86.1% to 92.9% (an increase of 6.8 percentage points), with a 90.9% balanced accuracy and 94.6% F1-score.

A two-stage deep learning pipeline integrating a Denoising Autoencoder (DAE) with a CNN classifier to suppress imaging artifacts, achieving 92.9% accuracy (+6.8 percentage points over baseline) and 90.9% balanced accuracy on the study dataset.

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Additional Featured Research

View all publications (6) →

Conference Paper•2026•★ Featured

Hybrid Deep Learning and Radiomic Shape Feature Fusion for Automated Brain Tumor Classification from MRI Images

Md Rashidul Islam, Abdulla Al Mobassir, Md Abdul Mutalib, Anamul Haque, Md. Abul Ala Moududi, Rakibul Islam, Md Mehedi Hasan, Nafizur Rahman, Najmul Haque, Abu Saleh Musa Miah, Md. Saiful Islam

2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), pp. 1-6

A hybrid deep learning and radiomic shape feature fusion framework leveraging EfficientNetB0 with dual spatial pooling to achieve 94% binary and 84.5% multiclass brain tumor classification on MRI benchmark scans.

Medical AIDeep LearningBrain TumorMRIFeature Fusion

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Book Chapter•2026•★ Featured

A Semi-Supervised and Explainable Stacked Ensemble Model for Multi-Class Haematological Disorder Classification Using Complete Blood Count Data

Kopil Das, Noshin Amina, Md Abdul Mutalib, Nasrin Chowdhury, Mohaiminul Islam

Taylor & Francis Book Chapter, pp. 1-16

Proposes a semi-supervised multi-class classification pipeline using Complete Blood Count (CBC) data with Label Spreading, SMOTE-Tomek resampling, and a seed-averaged stacked ensemble (RF, Extra Trees, SVC, Logistic Regression, Gradient Boosting, XGBoost), achieving 94.94% accuracy with SHAP explainability.

Medical AISemi-Supervised LearningHaematologyExplainable AI (XAI)Ensemble Learning

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Conference Paper•2025•★ Featured

Machine Learning-Based Air Quality Forecasting in Key Urban-Industrial Zones of Bangladesh

Md. Anamul Hossain Chowdhury, Rumi Chowdhury, Md Abdul Mutalib, Rayhan Mahmud Rakib, Pujan Dey Anik, Saeid Islam Tanvir

16th International Conference on Computing, Communication and Networking Technologies (ICCCNT 2025), pp. 1-7

Investigates machine learning forecasting of the Air Quality Index (AQI) across industrial and urban regions in Bangladesh over a decade-long dataset (2013–2022) with Random Forest, Decision Trees, KNN, and XGBoost, achieving R²=0.997 and MAE=0.349 with XGBoost.

Machine LearningEnvironmental AIAir Quality ForecastingXGBoostUrban Analytics

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Conference Paper•2025•First Author•★ Featured

Assessment and Prediction of Organic Pollution Index Using Deep Learning Models: A Case Study of Major Lakes in Dhaka City, Bangladesh

Md Abdul Mutalib, Md. Anamul Hossain Chowdhury, Rayhan Mahmud Rakib, Rumi Chowdhury, Saeid Islam Tanvir, Md Abdur Rashid Sarker, Md. Fahad Jubayer

16th International Conference on Computing, Communication and Networking Technologies (ICCCNT 2025), pp. 1-7

Evaluates and forecasts the Organic Pollution Index (OPI) across three major urban lakes in Dhaka City (Dhanmondi, Hatirjheel, and Gulshan) over six years (2019–2024) using CNN, TabNet, CNN-LSTM, and XGBoost architectures, achieving R²=0.9969 with CNN.

Deep LearningEnvironmental AIWater Quality PredictionCNN-LSTMTabNet

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Journal Article•2025•★ Featured

Hand Gesture Recognition Systems: A Review of Methods, Datasets, and Emerging Trends

Md Selim Sarowar, Nur E Jannatul Farjana, Md. Asraful Islam Khan, Md Abdul Mutalib, Syful Islam, Mohaiminul Islam

International Journal of Computer Applications (IJCA), Vol. 187, No. 2, pp. 1-33

A comprehensive review analyzing 252 peer-reviewed studies on Hand Gesture Recognition (HGR), spanning rule-based algorithms to 3D CNNs, Graph Convolutional Networks, benchmark datasets, and real-time edge constraints.

Computer VisionDeep LearningGesture RecognitionHuman-Computer Interaction

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Curriculum Vitae, Contact & Profiles

Curriculum Vitae

Detailed record of graduate research at HITSZ, undergraduate education at NEUB, published peer-reviewed papers, awards, and technical skills.

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Contact & Collaboration

Open to prospective PhD supervision inquiries, research collaborations, and academic discussions in Medical AI and 3D Computer Vision.

✉️ mutalib.cs@gmail.comBio-Computing Research Center, Harbin Institute of Technology, Shenzhen (HITSZ)

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