Classification of Healthy Control and Abnormal Lung Chest Radiography images using CBIR and Atlas-Based Graph cut Segmentation by Transfer Learning CNNs

Abstract

The prominent aim of systematic lung pathologies detection is to improve health outcomes among people with pulmonary diseases and decrease thorax diseases disseminating among the peoples through improved lung abnormality detection, reduction in diagnostic delays and early treatment. Today technological breakthroughs have led to the discovery of various invasive clinical imaging methods for the analysis of lung diseases. In this work, an integrated Computer-Aided Diagnoses (CAD) based on Deep Learning (DL) to diagnosis of lung diseases on chest radiographs was proposed.  In this paper we have used JSRT datasets it contains 247 images are employed atlas-based graph-cut segmentation algorithm for extract the lung regions and segmented regions are input to the Convolutional Neural Network (CNN) with augmentation techniques that are provides the training accuracy is 97% and 89% for testing accuracy.