A Convolutional Neural Network-Based Auto-Segmentation Pipeline for Breast Cancer Imaging

Lucas Jian Hoong Leow, Abu Bakr Azam, Hong Qi Tan, Wen Long Nei, Qi Cao, Lihui Huang, Yuan Xie and Yiyu Cai ()
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Lucas Jian Hoong Leow: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Abu Bakr Azam: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Hong Qi Tan: National Cancer Center, Singapore 168583, Singapore
Wen Long Nei: National Cancer Center, Singapore 168583, Singapore
Qi Cao: School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK
Lihui Huang: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Yuan Xie: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Yiyu Cai: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore

Mathematics, 2024, vol. 12, issue 4, 1-27

Abstract: Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline.

Keywords: convolutional neural network; 3D computed tomography scan; breast cancer; 3D U-Net architecture; 3D volumetric prediction pipeline (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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