TY - JOUR ID - 2404 TI - Automatic Detection of Lung Nodules on Computer Tomography Scans with a Deep Direct Regression Method JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Aghajani, Kh. AD - Department of Computer Engineering, University of Mazandaran, Babolsar, Iran. Y1 - 2022 PY - 2022 VL - 10 IS - 2 SP - 207 EP - 215 KW - Lung Nodule detection KW - Direct Regression KW - deep learning DO - 10.22044/jadm.2022.11431.2303 N2 - Deep-learning-based approaches have been extensively used in detecting pulmonary nodules from computer Tomography (CT) scans. In this study, an automated end-to-end framework with a convolution network (Conv-net) has been proposed to detect lung nodules from CT images. Here, boundary regression has been performed by a direct regression method, in which the offset is predicted from a given point. The proposed framework has two outputs; a pixel-wise classification between nodule or normal and a direct regression which is used to determine the four coordinates of the nodule's bounding box. The Loss function includes two terms; one for classification and the other for regression. The performance of the proposed method is compared with YOLOv2. The evaluation has been performed using Lung-Pet-CT-DX dataset. The experimental results show that the proposed framework outperforms the YOLOv2 method. The results demonstrate that the proposed framework possesses high accuracies of nodule localization and boundary estimation. UR - https://jad.shahroodut.ac.ir/article_2404.html L1 - https://jad.shahroodut.ac.ir/article_2404_962b19fbf305454704f8841902329eff.pdf ER -