NDLI: Deep3DSCan: Deep residual network and morphological descriptor based framework for lung cancer classification and 3D segmentation

Content Provider	IET Digital Library
Author	Bansal, Gaurang Chamola, Vinay Narang, Pratik Kumar, Subham Raman, Sundaresan
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.
Starting Page	1240
Ending Page	1247
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 7, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1164
Journal	IET Image Processing
Publisher Date	2020-03-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Benign Nodules Biology And Medical Computing Biomedical Imaging/measurement Cancer Cancerous Lesions Computer Aided Design Computer Vision And Image Processing Technique Computerised Tomography Deep Features Deep Learning Technique Deep Residual Network Domain Agnostic Nature Domain-specific Features Early Diagnosis Ensemble Framework Deep3DSCan Feature Extraction Fine-tuned Residual Network Fused Feature Handcrafted Descriptors Image Classification Image Fusion Image Matching Image Recognition Image Segmentation Increasing Incidence Rate Knowledge Engineering Technique Learning in AI Low Contrast Variation Lung Lung Cancer Classification Lung Cancer Patients Lung Cancer Segmentation Malignant Nodules Medical Image Processing Minute Differences Morphological Descriptor Based Framework Morphological Technique Mortality Rate Natural Image Segmentation Patient Diagnostic Method And Instrumentation Publicly Available LUNA16 Dataset Radiography And Computed Tomography Reasonable Scale Invariance Template Matching Technique Unseen Situations Visual Similarity X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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