An Efficient DA-Net Architecture for Lung Nodule Segmentation

Muazzam Maqsood, Sadaf Yasmin, Irfan Mehmood, Maryam Bukhari and Mucheol Kim
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Muazzam Maqsood: Department of Computer Science, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan
Sadaf Yasmin: Department of Computer Science, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan
Irfan Mehmood: Centre for Visual Computing, University of Bradford, Bradford BD7 1DP, UK
Maryam Bukhari: Department of Computer Science, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan
Mucheol Kim: School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, Korea

Mathematics, 2021, vol. 9, issue 13, 1-16

Abstract: A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

Keywords: lung nodule segmentation; online diagnosis; DA-Net; Atrous convolutions; unsupervised learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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