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Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier

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Abstract

This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules. A 0.3 confidence threshold was established for identifying nodules in this phase to enhance the model's sensitivity. The primary objective of the hierarchical model was to locate and categorize all lung nodules while minimizing the false-negative rate. Following the analysis of the results from the first phase, a novel 3D convolutional neural network (CNN) classifier was developed to examine and categorize the potential nodules detected by the YOLOv5s model. The objective was to create a detection framework characterized by an extremely low false positive rate and high accuracy. The Lung Nodule Analysis 2016 (LUNA 16) dataset was used to evaluate the effectiveness of this framework. This dataset comprises 888 CT scans that include the positions of 1186 nodules and 400,000 non-nodular regions in the lungs. The YOLOv5s technique yielded numerous incorrect detections owing to its low confidence level. Nevertheless, the addition of a 3D classification system significantly enhanced the precision of nodule identification. By integrating the outcomes of the YOLOv5s approach using a 30% confidence limit and the 3D CNN classification model, the overall system achieved 98.4% nodule detection accuracy and an area under the curve of 98.9%. Despite producing some false negatives and false positives, the suggested method for identifying lung nodules from CT scans is promising as a valuable aid in decision-making for nodule detection.

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Funding

We would like to state that no external funding or financial support was received for this research. The study was conducted independently without any monetary contributions from any organization or entity.

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Authors and Affiliations

  1. Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran

    Yashar Ahmadyar, Alireza Kamali-Asl & Rezvan Samimi

  2. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland

    Hossein Arabi & Habib Zaidi

  3. Geneva University Neurocenter, Geneva University, 1205, Geneva, Switzerland

    Habib Zaidi

  4. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Habib Zaidi

  5. Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark

    Habib Zaidi

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  1. Yashar Ahmadyar
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  2. Alireza Kamali-Asl
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  4. Rezvan Samimi
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Correspondence to Alireza Kamali-Asl.

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All procedures performed were in accordance with the ethical standards of the internal institution’s ethical committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Ahmadyar, Y., Kamali-Asl, A., Arabi, H. et al. Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier. Radiol Phys Technol 17, 124–134 (2024). https://doi.org/10.1007/s12194-023-00756-9

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  • DOI: https://doi.org/10.1007/s12194-023-00756-9

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