Conference Paper2026

Deep Learning Framework for Automated Pulmonary Nodule Identification and Anatomical Segmentation in Thoracic CT Scans

Md Yousuf, Abbas Yasin, Muaaz Mohammad, Fatin Israq, Siam Tahsin Bhuiyan, Rashedur Rahman, Saadia Binte Alam, Ashraful Islam

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

IEEE, pp. 1-6

  • Good Health and Well-being — Relevance: 95%
DOI: 10.1109/qpain69676.2026.11545645URL

Abstract

Pulmonary malignancies constitute a significant global health challenge, with low-dose computed tomography screening demonstrating considerable effectiveness in reducing mortality rates. However, manual radiological evaluation remains time-intensive and prone to variability between observers, especially when assessing subtle parenchymal abnormalities. This study introduces an integrated computational pipeline utilizing encoder-decoder neural network architectures for simultaneous delineation of lung structures and nodular regions from thoracic imaging data. The proposed approach incorporates specialized image enhancement through Hounsfield unit windowing tailored for parenchymal tissue visualization. A U-Net architecture with ResNet-18 feature extraction backbone facilitates pixel-level classification across four anatomical categories. Experimental evaluation on 888 cases from the LUNA16 dataset achieved mean intersection-over-union scores of 0.994 for left lung region, 0.960 for right lung region, and 0.741 for nodular structures, with aggregate Dice similarity coefficient reaching 0.947. Crosssubset validation confirmed model consistency with minimal performance variation across held-out partitions.

Keywords

  • chest CT
  • lung nodule detection
  • U-Net
  • LUNA16 dataset
  • low-dose CT screening
  • encoder-decoder architecture
  • lung segmentation

CCDS Authors

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

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