Performance of a bank of 2D efficient-nets for benign/malignant classification of lung nodules in 3D thoracic x-ray CT scans

Tyler Settle; Benjamin Veasey; Albert Seow; Amir A Amini

doi:10.1117/12.2657009

10 April 2023 Performance of a bank of 2D efficient-nets for benign/malignant classification of lung nodules in 3D thoracic x-ray CT scans

Tyler Settle, Benjamin Veasey, Albert Seow, Amir A Amini

Author Affiliations +

Proceedings Volume 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging; 124681G (2023) https://doi.org/10.1117/12.2657009
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Lung cancer is the leading cause of cancer-related death among both men and women and second most commonly diagnosed cancer, accounting for 18% of the total cancer deaths world-wide [4]. Screening high-risk patients with low dose Computed Tomography (CT) can lead to earlier treatment and increase the survival rate [5]. However, cancer diagnosis remains a challenging problem due to the subtle visual differences between benign and malignant nodules in CT images. Hence, computer-aided diagnosis (CADx) systems may prove useful in assisting radiologists in the malignancy prediction task. Previously we developed a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-timepoint classification in a Siamese structure [6]. The use of a sequence of parallel 2-D CNNs in place of a 3D CNN will result in significant reduction in the number of network parameters. In this paper, we keep the same overall structure utilized in [6] including the attention mechanism. However, herein, we report on use of Efficient-Net [1] for 2-D feature extractors due to its success on the Image-Net classification challenge. Variations of the Efficient-Net B0 to B7 pretrained on Image-Net were fine-tuned and applied to NLSTx data [6] a subset of data acquired in the National Lung Screening Trial (NLST) [2]. NLSTx includes data from biopsy confirmed scans in 650 benign and 207 malignant nodules at up to 3 time points. In our study, the performance of the best Efficient- Net reached an area under ROC curve of .7896 for the benign/malignant classification.

Citation Download Citation

Tyler Settle, Benjamin Veasey, Albert Seow, and Amir A Amini "Performance of a bank of 2D efficient-nets for benign/malignant classification of lung nodules in 3D thoracic x-ray CT scans", Proc. SPIE 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, 124681G (10 April 2023); https://doi.org/10.1117/12.2657009

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