Fast-staged CNN Model for Accurate pulmonary diseases and Lung cancer detection

• URL: http://arxiv.org/abs/2412.11681v1
• Date: Mon, 16 Dec 2024 11:47:07 GMT
• Title: Fast-staged CNN Model for Accurate pulmonary diseases and Lung cancer detection
• Authors: Abdelbaki Souid, Mohamed Hamroun, Soufiene Ben Othman, Hedi Sakli, Naceur Abdelkarim,
• Abstract summary: This research evaluates a deep learning model designed to detect lung cancer, specifically pulmonary nodules, along with eight other lung pathologies, using chest radiographs. A two-stage classification system, utilizing ensemble methods and transfer learning, is employed to first triage images into Normal or Abnormal. The model achieves notable results in classification, with a top-performing accuracy of 77%, a sensitivity of 0.713, a specificity of 0.776 during external validation, and an AUC score of 0.888.
• Score: 0.0
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• Abstract: Pulmonary pathologies are a significant global health concern, often leading to fatal outcomes if not diagnosed and treated promptly. Chest radiography serves as a primary diagnostic tool, but the availability of experienced radiologists remains limited. Advances in Artificial Intelligence (AI) and machine learning, particularly in computer vision, offer promising solutions to address this challenge. This research evaluates a deep learning model designed to detect lung cancer, specifically pulmonary nodules, along with eight other lung pathologies, using chest radiographs. The study leverages diverse datasets comprising over 135,120 frontal chest radiographs to train a Convolutional Neural Network (CNN). A two-stage classification system, utilizing ensemble methods and transfer learning, is employed to first triage images into Normal or Abnormal categories and then identify specific pathologies, including lung nodules. The deep learning model achieves notable results in nodule classification, with a top-performing accuracy of 77%, a sensitivity of 0.713, a specificity of 0.776 during external validation, and an AUC score of 0.888. Despite these successes, some misclassifications were observed, primarily false negatives. In conclusion, the model demonstrates robust potential for generalization across diverse patient populations, attributed to the geographic diversity of the training dataset. Future work could focus on integrating ETL data distribution strategies and expanding the dataset with additional nodule-type samples to further enhance diagnostic accuracy.

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