LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering

• URL: http://arxiv.org/abs/2505.06370v1
• Date: Fri, 09 May 2025 18:25:59 GMT
• Title: LMLCC-Net: A Semi-Supervised Deep Learning Model for Lung Nodule Malignancy Prediction from CT Scans using a Novel Hounsfield Unit-Based Intensity Filtering
• Authors: Adhora Madhuri, Nusaiba Sobir, Tasnia Binte Mamun, Taufiq Hasan,
• Abstract summary: We propose LMLCC-Net, a novel deep learning framework for classifying nodules from CT scan images using a 3D CNN.<n>Our proposed method achieves a classification accuracy (ACC) of 91.96%, a sensitivity (SEN) of 92.04%, and an area under the curve (AUC) of 91.87%.
• Score: 1.2974519529978974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lung cancer is the leading cause of patient mortality in the world. Early diagnosis of malignant pulmonary nodules in CT images can have a significant impact on reducing disease mortality and morbidity. In this work, we propose LMLCC-Net, a novel deep learning framework for classifying nodules from CT scan images using a 3D CNN, considering Hounsfield Unit (HU)-based intensity filtering. Benign and malignant nodules have significant differences in their intensity profile of HU, which was not exploited in the literature. Our method considers the intensity pattern as well as the texture for the prediction of malignancies. LMLCC-Net extracts features from multiple branches that each use a separate learnable HU-based intensity filtering stage. Various combinations of branches and learnable ranges of filters were explored to finally produce the best-performing model. In addition, we propose a semi-supervised learning scheme for labeling ambiguous cases and also developed a lightweight model to classify the nodules. The experimental evaluations are carried out on the LUNA16 dataset. Our proposed method achieves a classification accuracy (ACC) of 91.96%, a sensitivity (SEN) of 92.04%, and an area under the curve (AUC) of 91.87%, showing improved performance compared to existing methods. The proposed method can have a significant impact in helping radiologists in the classification of pulmonary nodules and improving patient care.

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