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Deep3DSCan: Deep residual network and morphological descriptor based framework forlung cancer classification and 3D segmentation

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dc.contributor.author Raman, Sundaresan
dc.contributor.author Chamola, Vinay
dc.contributor.author Narang, Pratik
dc.date.accessioned 2023-01-05T10:19:02Z
dc.date.available 2023-01-05T10:19:02Z
dc.date.issued 2020-04
dc.identifier.uri https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-ipr.2019.1164
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8316
dc.description.abstract With the increasing incidence rate of lung cancer patients, early diagnosis could help in reducing the mortality rate. However, accurate recognition of cancerous lesions is immensely challenging owing to factors such as low contrast variation, heterogeneity and visual similarity between benign and malignant nodules. Deep learning techniques have been very effective in performing natural image segmentation with robustness to previously unseen situations, reasonable scale invariance and the ability to detect even minute differences. However, they usually fail to learn domain-specific features due to the limited amount of available data and domain agnostic nature of these techniques. This work presents an ensemble framework Deep3DSCan for lung cancer segmentation and classification. The deep 3D segmentation network generates the 3D volume of interest from computed tomography scans of patients. The deep features and handcrafted descriptors are extracted using a fine-tuned residual network and morphological techniques, respectively. Finally, the fused features are used for cancer classification. The experiments were conducted on the publicly available LUNA16 dataset. For the segmentation, the authors achieved an accuracy of 0.927, significant improvement over the template matching technique, which had achieved an accuracy of 0.927. For the detection, previous state-of-the-art is 0.866, while ours is 0.883. en_US
dc.language.iso en en_US
dc.publisher IET en_US
dc.subject Computer Science en_US
dc.subject Deep3DSCan en_US
dc.subject 3D segmentation en_US
dc.subject Lung cancer en_US
dc.title Deep3DSCan: Deep residual network and morphological descriptor based framework forlung cancer classification and 3D segmentation en_US
dc.type Article en_US


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