Citation

Rashid, Umar and Sattar, Kalim and Ashraf, Rehan and Rehman, Aqeel ur and Zahra, Syeda Zoupash and Bashir, Rab Nawaz and Kamal, Shahid and Rehman, Amjad (2026) Multi‐Stage Deep Learning Model for the Detection and Classification of Lung Nodules From CT Scan Images. IET Image Processing, 20 (1). ISSN 1751-9659

Text IET Image Processing - 2026 - Rashid - Multi‐Stage Deep Learning Model for the Detection and Classification of Lung Nodules.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Early detection of pulmonary nodules in computed tomography (CT) scans is crucial for improving lung cancer diagnosis and survival rates. We propose a novel multi-stage deep learning framework for automated lung nodule detection and classification, addressing limitations in existing methods such as low sensitivity and high false-positive rates. Our approach comprises three phases: (1) lung region segmentation using a modified pyramid scene parsing network (PSPNet) with enhanced contextual feature extraction, (2) candidate nodule extraction via a redesigned U-Net architecture optimized for small nodule detection, and (3) classification of nodules as benign or malignant using a tailored ResNet-50 with improved gradient flow. We trained and evaluated the framework on the LUNA16 dataset, comprising 888 CT scans with 1186 annotated nodules, split into 710 scans for training, 128 for validation, and 50 for testing. Our model achieved an accuracy of 95.25% and an area under the ROC curve (AUC) of 0.97, outperforming state-of-the-art methods by 3.5% in accuracy and reducing false positives by 12%. These results highlight the framework’s robustness and precision, offering a reliable solution for early lung nodule detection with significant potential for clinical lung cancer screening applications.

Item Type:	Article
Uncontrolled Keywords:	human health, nodules
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 04:09
Last Modified:	04 Aug 2026 04:09
URII:	http://shdl.mmu.edu.my/id/eprint/16480

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