Open AccessAutomatic Classification and Accuracy by Deep Learning Using CNN Methods in Lung Chest X-Ray Images
Author(s) -
V. Thamilarasi,
R. Roselin
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1055/1/012099
Subject(s) - artificial intelligence , computer science , segmentation , deep learning , pattern recognition (psychology) , contextual image classification , convolutional neural network , image segmentation , radiography , medical imaging , computer vision , process (computing) , radiology , image (mathematics) , medicine , operating system
Automatic image segmentation and classification of medical images plays significant role in detection and diagnosis of various pathological process. Normally chest radiography is a basic representation to find many abnormalities present in the chest. Radiology services delayed due to proper detection, segmentation and classification of diseases. Automatic segmentation and classification of medical images improved both pathological and radiological process. In recent days the deep learning with CNN methods provides remarkable successes in medical image diagnosis with in time limit and with minimum cost. The proposed method handles CNN for automatic classification of lung chest x-ray images as normal and up normal. Applying these modern techniques to lung chest x-ray images face more challenges while using small dataset. For testing JSRT dataset used which contains 247 images. Preeminent performance achieved using 180 images of nodule and non-nodule images. This method produce expected classification accuracy with the help of faster computation of CNN within fraction of seconds and attain 86.67% in classification accuracy.