Learning from an Imbalanced and Limited Dataset and an Application to Medical Imaging Chest Xrays (CXRs) are routinely acquired in medical imaging for the purpose of diagnosing lung diseases. But for many patients, accurate and timely radiologic interpretation of the acquired CXRs is not always feasible, due to limited medical personnel and resources. A computer aided diagnosis (CAD) system based on machine learning would be an effective solution to enhance the efficiency of disease diagnosis. However, obtaining a sufficiently largescale, balanced, and annotated dataset of CXRs for effectively training a CAD system is challenging in practice. In this paper, we present a comprehensive comparative study on learning from imbalanced and limited CXRs to detect pneumonia, tackling two main questions: (1) Is data sampling an effective method for improving the performance of learning models? (2) Are there quantifiable differences between learning models with different sampling techniques? With respect to data sampling, we investigate two general categories of techniques that modify of an imbalanced data set to deliver a balanced data distribution: (i) undersampling the majority class; and (ii) oversamplingaugmentation of the minority class. With respect to learning models, we focus on Support Vector Machine (SVM) and deep convolutional neural network (CNN). Using a publicly available CXR dataset, we demonstrate that SVM and CNN learning models both exhibit improved performance, with the proper selection of the data sampling strategies.

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.