%0 Journal Article %T Automatic Detection of Lung Nodules on Computer Tomography Scans with a Deep Direct Regression Method %J Journal of AI and Data Mining %I Shahrood University of Technology %Z 2322-5211 %A Aghajani, Kh. %D 2022 %\ 04/01/2022 %V 10 %N 2 %P 207-215 %! Automatic Detection of Lung Nodules on Computer Tomography Scans with a Deep Direct Regression Method %K Lung Nodule detection %K Direct Regression %K deep learning %R 10.22044/jadm.2022.11431.2303 %X 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. %U https://jad.shahroodut.ac.ir/article_2404_962b19fbf305454704f8841902329eff.pdf