A bi-directional deep learning architecture for lung nodule semantic segmentation A bi-directional deep learning architecture for lung nodule semantic segmentation

• Published in: The Visual computer Vol. 39; no. 11; pp. 5245 - 5261
• Main Authors: Bhattacharyya, Debnath, Thirupathi Rao, N., Joshua, Eali Stephen Neal, Hu, Yu-Chen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Automation; Computed tomography; Computer Graphics; Computer Science; Datasets; Deep learning; Illnesses; Image Processing and Computer Vision; Lesions; Lung cancer; Machine learning; Magnetic resonance imaging; Medical imaging; Medical personnel; Neural networks; Nodules; Original; Original Article; Semantic segmentation; Semantics; Tomography; Tumors; Women; United States > US; Deep learning; Lung cancer; Bidirectional feature extraction; Convolutional neural network; Computer-aided diagnosis
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-022-02657-1

Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. I...