MetaLung: Meticulous affine-transformation-based lung cancer augmentation method
Nam D. Panina A. Pak A. Hajiyev F.
October 2024Institute of Advanced Engineering and Science
Indonesian Journal of Electrical Engineering and Computer Science
2024#36Issue 1401 - 413 pp.
The limitation of medical image data in open source is a big challenge for medical image processing. Medical data is closed because of confidential and ethical issues, also manual labeling of medical data is an expensive process. We propose a new augmentation method named MetaLung (Meticulous affine-transformation-based lung cancer augmentation method) for lung CT image augmentation. The key feature of the proposed method is the ability to expand the training dataset while preserving clinical and instrumental features. MetaLung shows a stable increase in image segmentation quality for three CNN-based models with different computational complexity (U-Net, DeepLabV3, and MaskRCNN). Also, the method allows in reduce the number of False Positive predictions.
Affine transformation , Data augmentation , Image segmentation , Lung cancer , Medical image processing
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School of Information Technology and Engineering, Kazakh-British Technical University (KBTU), Almaty, Kazakhstan
Department of Radiology and Nuclear Medicine, Kazakh Research Institute of Oncology and Radiology, Almaty, Kazakhstan
School of Information Technologies and Engineering, ADA University, Baku, Azerbaijan
School of Information Technology and Engineering
Department of Radiology and Nuclear Medicine
School of Information Technologies and Engineering
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