Segmentation-Guided Hybrid Deep Learning for Pulmonary Nodule Detection and Risk Prediction from Multi-Cohort CT Images
Gomavarapu Krishna Subramanyam, Kundojjala Srinivas, Veera Venkata Raghunath Indugu, Dedeepya Sai Gondi, Sai Krishna Gaduputi Subbammagari
TL;DR
This paper introduces a deep learning framework that improves lung cancer screening by accurately detecting and predicting the risk of pulmonary nodules in CT scans.
Contribution
A novel dual-stage framework combining segmentation-guided detection and a hybrid CNN–Transformer classifier for improved nodule localization and malignancy risk estimation.
Findings
The framework achieved a CPM of 0.944 for nodule detection on the LUNA16 dataset.
Malignancy classification on LIDC-IDRI reached a ROC-AUC of 0.988 and a PR-AUC of 0.947.
Calibration metrics showed strong reliability with an expected calibration error of 0.209.
Abstract
Background: Lung cancer screening using low-dose computed tomography (LDCT) demands not only early pulmonary nodule detection but also accurate estimation of malignancy risk. This remains challenging due to subtle nodule appearances, the large number of CT slices per scan, and variability in radiological interpretation. The objective of this study is to develop a unified computer-aided detection and diagnosis framework that improves both nodule localization and malignancy assessment while maintaining clinical reliability. Methods: We propose Seg-CADe-CADx, a dual-stage deep learning framework that integrates segmentation-guided detection and malignancy classification. In the first stage, a segmentation-guided detector with a lightweight 2.5D refinement head is employed to enhance nodule localization accuracy, particularly for small nodules with diameters of 6 mm or less. In the second…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsLung Cancer Diagnosis and Treatment · COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging