Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation.

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images.

A Novel Hybridized Feature Extraction Approach for Lung Nodule Classification Based on Transfer Learning Technique.

Purpose: In the field of medical diagnosis, deep learning-based computer-aided detection of diseases will reduce the burden of physicians in the diagnosis of diseases especially in the case of lung cancer nodule classification.

Materials And Methods: A hybridized model which integrates deep features from Residual Neural Network using transfer learning and handcrafted features from the histogram of oriented gradients feature descriptor is proposed to classify the lung nodules as benign or malignant. The intrinsic convolutional neural network (CNN) features have been incorporated and they can resolve the drawbacks of handcrafted features that do not completely reflect the specific characteristics of a nodule.