mathematics Article An Efficient DA-Net Architecture for Lung Nodule Segmentation Muazzam Maqsood 1, *, Sadaf Yasmin 1 , Irfan Mehmood 2 , Maryam Bukhari 1 and Mucheol Kim 3, *   Citation: Maqsood, M.; Yasmin, S.; Mehmood, I.; Bukhari, M.; Kim, M. An Efficient DA-Net Architecture for Lung Nodule Segmentation. Mathematics 2021, 9, 1457. https:// doi.org/10.3390/math9131457 Academic Editor: Bo-Hao Chen Received: 4 May 2021 Accepted: 2 June 2021 Published: 22 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Computer Science, COMSATS University Islamabad Attock Campus, Attock 43600, Pakistan; sadaf_yasmin@cuiatk.edu.pk (S.Y.); sp21-rcs-008@cuiatk.edu.pk (M.B.) 2 Centre for Visual Computing, University of Bradford, Bradford BD7 1DP, UK; i.mehmood4@bradford.ac.uk 3 School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, Korea * Correspondence: muazzam.maqsood@cuiatk.edu.pk (M.M.); mucheol.kim@gmail.com (M.K.) 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; unsuper- vised learning 1. Introduction Statistical data reveal that lung cancer is an incurable disease with a worldwide sur- vival rate of around 18% for only five years [1]. The nature of this disease requires diagnosis before time, and proper treatment planning is also necessary for better treatment [2]. Over time, advancement has been witnessed in computer-aided diagnostic (CAD) systems, but there have been fewer improvements in CAD systems specifically in terms of lung nodule detection thus far [3–5]. Due to the complexity of the disease, detection of cancer tends to be inaccurate, eventually affecting diagnosis and treatment planning. Computed tomography (CT) plays a vital part in the diagnosis as well as treatment of lung nodule cancer [6]. However, as data are expanding with time, CT images are also rising in quantity. With the growing number of images, it becomes challenging to move towards manual lung nodule segmentation procedures. Considering this issue, we require automatic segmentation procedures, and it is important to move towards this area [7]. The structure and location of lung nodules make lung nodule detection a difficult problem [8–10]. Lung nodule detection is very demanding because of the way nodules are structured and situated inside the pulmonary region. Often, there is an indistinguishable color contrast among lung nodules and neighboring regions on CT images, which makes it difficult to design a generic segmentation method. The juxtapleural nodules are distinctively responsible for exhibiting identical color contrast as of lung wall, and manual approaches provide inaccurate results in this case. An additional complication is with achieving accurate segmentation of jux- tavascular nodules since these are directly linked to blood vessels in the lung parenchyma. Mathematics 2021, 9, 1457. https://doi.org/10.3390/math9131457 https://www.mdpi.com/journal/mathematics