Published:

• M. N. Hasan, S. B. Shuvo, M. M. H. Ankon, S. M. T. U. Raju, and N. Siddique, “TransfusionNet: Framework for cervical cancer detection using deep learning with multi-level fusion", Results in Engineering, vol. 28, p. 107174, 2025, doi: 10.1016/j.rineng.2025.107174.

Under Review:

• M. N. Hasan, I. Ahmad, S. B. Shuvo, M. M. H. Ankon, S. Das, N. Siddique, and H. Wang, “DL3M: A Visionto-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models,” arXiv preprint arXiv:2512.13742, 2025. Under review in Medical Image Analysis, Elsevier.

In Preparation:

• S. B. Shuvo, S. A. Redhila, S. Hossain,  and A. D. Roy, “NeuroSwin: A Swin Transformer-GRU Model for Parkinson's Disease Classification and Brain Region Localization from EEG Signals,” Manuscript in preparation for Biomedical Signal Processing and Control (Elsevier).

• S. B. Shuvo, M. M. H. Ankon, M. A. Sayed, M. N. Hasan, and M. Z. Chowdhury, “CholeNet: A Recurrent-Convolutional Hybrid Ensemble Framework with Quad-Fold Parallel Transfer Learning and Spatio-Sequential Feature Fusion for Gallbladder Cancer Classification,” Manuscript in preparation for Biomedical Signal Processing and Control (Elsevier).

• M. M. H. Ankon, S. B. Shuvo, M. N. Hasan, and M. M. H. Manik, “VRX-UNet: Automated Volumetric Characterization of Intra-tumoral Structures in Gliomas using 3D Segmentation,” Manuscript in preparation for Computers in Biology and Medicine (Elsevier).

• M. A. Sayed, M. A. Rahman, S. B. Shuvo, M. J. Hossain, M. S. Alam, K. N. Hasan, and M. A. Kadir, “Advanced image analysis for liver tumor detection and visualization in CT images using automated segmentation,” Manuscript in preparation for Nature - Scientific Reports.

• M. N. Hasan,  M. M. H. Ankon, S. B. Shuvo, I. Ahmad, M. M. H. Manik, and N. Siddique, “Zero-to-Multi-Shot Benchmarking of Model-Agnostic Meta-Learning (MAML)-Based Domain Adaptation for Multi-Cancer Classification: Comparative Evaluation of LLMs, State-of-the-Art Deep Learning Models, and a Proposed Hybrid Approach,” Ongoing Research.

• M. N. Hasan, W. M. Shafin, T. Joty, Md. M. H. Ankon, S. B. Shuvo, and N. Siddique, “Unveiling Overfitting Patterns: Explainable AI-Driven Benchmarking of LLMs, Deep Learning Models, and a Hybrid Framework for Medical Image Classification,” Ongoing Research

Published:

• S. B. Shuvo and M. Z. Chowdhury, "Classification of Gallbladder Cancer Using Average Ensemble Learning," 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), Dhaka, Bangladesh, 2024, pp. 1450-1455, doi: 10.1109/ICEEICT62016.2024.10534480. [PDF]

• S. B. Shuvo, S. Hossain, S. A. Redhila and U. Bose, "Exploring Common Molecular Interactions across Multiple Cancers to Identify Potential Therapeutic Targets and Drug Candidates," 2025 7th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 2025, pp. 1-6, doi: 10.1109/EICT68394.2025.11355627. [PDF]

Under Review:

• Md. A. Sayed, A. Rahman, S. B. Shuvo, M. A. Kadir, Md. I. A. Imran, Md. S. I. Wadud, and Md. N. Hossain, “Automated PET to Fused PET-CT Mapping Using PCGAN for Lung Cancer Diagnosis,” Under Review in 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking, Chattogram, Bangladesh.

Oral presentation at ICEEICT (2024) on “Classification of Gallbladder Cancer Using Average Ensemble Learning.” (Presentation Slide)

Oral presentation at EICT (2025) on “Exploring Common Molecular Interactions across Multiple Cancers to Identify Potential Therapeutic Targets and Drug Candidates.” (Presentation Slide)

Event: 1st National Conference of Research, Industry and Collaboration in Biomedical Engineering

Organizer: Department of Biomedical Engineering, Jashore University of Science & Technology

Date: June 22, 2025

Format: Poster presentation

Title: Exploring Gene Network Interactions and Pathways in Three Cancers: Identifying Potential Therapeutic Targets and Drug Candidates

Poster Link: Google Drive

Certification Link: