Deep-learning-based approaches have been extensively used in detecting pulmonary nodules from computer tomography (CT) scans. In this work, an automated end-to-end framework with a convolution network (Conv-net) is proposed to detect lung nodules from the 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 that is used in order 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 YOLOv. The evaluation is performed using the Lung-Pet-CT DX dataset. The experimental results show that the proposed framework outperforms the YOLOv method. The results obtained demonstrate that the suggested framework possesses high accuracies of nodule localization and boundary estimation.
