{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/diagnostic-classification-of-lung-nodules","title":"Diagnostic Classification Of Lung Nodules Using 3D Neural Networks","arxiv_id":"1803.07192","date":"2018-03-19","proceeding":null,"authors":["Raunak Dey","Zhongjie Lu","Yi Hong"],"abstract":"Lung cancer is the leading cause of cancer-related death worldwide. Early\ndiagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides\nan opportunity for designing effective treatment and making financial and care\nplans. In this paper, we consider the problem of diagnostic classification\nbetween benign and malignant lung nodules in CT images, which aims to learn a\ndirect mapping from 3D images to class labels. To achieve this goal, four\ntwo-pathway Convolutional Neural Networks (CNN) are proposed, including a basic\n3D CNN, a novel multi-output network, a 3D DenseNet, and an augmented 3D\nDenseNet with multi-outputs. These four networks are evaluated on the public\nLIDC-IDRI dataset and outperform most existing methods. In particular, the 3D\nmulti-output DenseNet (MoDenseNet) achieves the state-of-the-art classification\naccuracy on the task of end-to-end lung nodule diagnosis. In addition, the\nnetworks pretrained on the LIDC-IDRI dataset can be further extended to handle\nsmaller datasets using transfer learning. This is demonstrated on our dataset\nwith encouraging prediction accuracy in lung nodule classification.","url_abs":"http://arxiv.org/abs/1803.07192v1","url_pdf":"http://arxiv.org/pdf/1803.07192v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"diagnostic-classification-of-lung-nodules","repo_url":"https://github.com/raun1/Diagnostic-Classification-Of-Lung-Nodules-Using-3D-Neural-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"diagnostic-classification-of-lung-nodules","repo_url":"https://github.com/raun1/ISBI2018-Diagnostic-Classification-Of-Lung-Nodules-Using-3D-Neural-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lung-nodule-classification","task_name":"Lung Nodule Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}