Lung Boundary Detection and Classification in Chest XRays Images Based on Neural Network The isolation of different structures is often performed on chest radiography (CXR) and the classification of abnormalities is an initial step in detection systems as computeraided diagnosis (CAD). The shape and size of lungs may hold clues to serious diseases such as pneumothorax, pneumoconiosis and even emphysema. More than 500,000 people die in the United States every year due to heart and lung failure, often being tested for the normal CXR film. With an increasing number of patients, the doctors must overwork, hence they cannot provide the advice and take care of their patients correctly. In this case, the computer system that supports image classification and boundary CXR detection is needed. This paper presents our automated approach for lung boundary detection and CXR classification in conventional poster anterior chest radiographs. We first extract the lung region, size measurements, and shape irregularities using segmentation techniques that are used in image processing on chest radiographs. For the CXR image, we extract 18 various features using the gray level cooccurrence matrix (GLCM) which enables the CXR to be classified as normal or abnormal using the probabilistic neural network (PNN) classifier. We measure the performance of our system using two data sets: the Montgomery County (MC) xray dataset and the Shenzhen Xray dataset. The proposed methodology has competitive results with relatively shorter training time and higher accuracy.

XLSor: A Robust and Accurate Lung Segmentor on Chest XRays Using CrissCross Attention and Customized Radiorealistic Abnormalities Generation This paper proposes a novel framework for lung segmentation in chest Xrays. It consists of two key contributions, a crisscross attention based segmentation network and radiorealistic chest Xray image synthesis (i.e. a synthesized radiograph that appears anatomically realistic) for data augmentation. The crisscross attention modules capture rich global contextual information in both horizontal and vertical directions for all the pixels thus facilitating accurate lung segmentation. To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological lungs with hazy lung boundaries, an imagetoimage translation module is employed to synthesize radiorealistic abnormal CXRs from the source of normal ones for data augmentation. The lung masks of synthetic abnormal CXRs are propagated from the segmentation results of their normal counterparts, and then serve as pseudo masks for robust segmentor training. In addition, we annotate 100 CXRs with lung masks on a more challenging NIH Chest Xray dataset containing both posterioranterior and anteroposterior views for evaluation. Extensive experiments validate the robustness and effectiveness of the proposed framework. The code and data can be found from this https URL .