iXray: A machine learning-based digital radiograph pattern recognition system for lung pathology detection

Radiography is a branch of Health Science that uses x-ray beams to picture out the bones and organs. Chest plain radiographs are used by experts to identify lung abnormalities using pattern recognition. Digitized x-ray images already available however, diagnosis, through the uses of pattern recognition, is done manually. In this research, the group presents a system that automates pattern recognition on digital chest radiographs utilizing image processing, feature extraction and machine learning algorithms, making early detection of symptoms of lung abnormalities more efficient. This paper focuses on 6 common lung conditions namely, Normal, Pleural Effusion, Pneumothorax, Cardiomegaly, Hyperaeration and Lung Nodules. The lung conditions were divided into 2: histogram-based (Normal, Pleural Effusion and Pneumothorax) and statistics-based (Possible Lung Nodules, Cardiomegaly and Hyperaeration). The database is composed of 743 x-ray images in total, 560 acquired from De La Salle-Health Sciences Institute (DLS-HIS) and 183 downloaded from the internet, which are all in TIFF image format. Furthermore, it follows a labeling scheme that is dependent on the lung condition it pertains to. Sequential Minimal Optimization, known in pattern recognition and is able to handle multi-class classification, is used for the modeling and classification of the histogram-based lung conditions. The SVM classifier is trained with features from 40 images each from the histogram-based lung conditions, and is tested 18 images. The statistics-based ling conditions are classified using logic operation. Classification of the histogram-based lung conditions implemented in WEKA showed 92.59% classification accuracy with both Radial Basis Function kernel and Polynomial kernel. For the statistics-based lung conditions, Normal vs Cardiomegaly attained an accuracy 0f 70%, Normal vs Hyperaeration attained an accuracy of 73.33%, and Normal vs Possible Lung Nodules attained an accuracy rate of 58.33%. The test performance of the system in classifying Normal vs Abnormal case achieved an accuracy of 67.22%. The system has to be modified to improve the classification for Cardiomegaly and possible Lung Nodules and to increase the False Negative rate of Normal vs Cardiomegaly which is Nodules and to increase the False Negative rate of Normal vs Cardiomegaly which is 33.33%

iXray: A machine learning-based digital radiograph pattern recognition system for lung pathology detection

A radiograph is a visualization aid that physicians use in identifying lung abnormalities. Although digitized x-ray images are available, diagnosis by a medical expert through pattern recognition is done manually. Thus, this paper presents a system that utilizes machine learning for pattern recognition and classification of six lung conditions. Classified into two categories, namely histogram-based (normal, pleural effusion, and pneumothorax) and statistics-based (cardiomegaly, hyperaeration, and possible lung nodules). Using preprocessing and feature extraction techniques, the designed system achieves an accuracy rate of 92.59% for the histogram-based lung conditions using Sequential Minimal Optimization (SMO) and 67.22% for the statistics-based lung conditions using logic operations. © Springer International Publishing AG, part of Springer Nature 2018

iXray: A machine learning-based digital radiograph pattern recognition system for lung pathology detection

A radiograph is a visualization aid that physicians use in identifying lung abnormalities. Although digitized x-ray images are available, diagnosis by a medical expert through pattern recognition is done manually. Thus, this paper presents a system that utilizes machine learning for pattern recognition and classification of six lung conditions classified into two categories, namely Histogram-based (Normal, Pleural Effusion, and Pneumothorax) and Statistics-based (Cardiomegaly, Hyperaeration, and possible Lung Nodules). Using preprocessing and feature extraction techniques, the designed system achieves an accuracy rate of 92.59% for the Histogram-based lung conditions using Sequential Minimal Optimization (SMO) and 67.22% for the Statistics-based lung conditions using logic operations. © 2015, Mechatronics and Machine Vision in Practice. All rights reserved

SMO-based system for identifying common lung conditions using histogram

A radiograph is a visualization aid that physicians use in identifying lung abnormalities. Although digitized x-ray images are available, diagnosis by a medical expert through pattern recognition is done manually. Thus, this paper presents a system that utilizes machine learning for pattern recognition and classification of three lung conditions, namely Normal, Pleural Effusion and Pneumothorax cases. Using two histogram equalization techniques, the designed system achieves an accuracy rate of 76.19% and 78.10% by using Sequential Minimal Optimization (SMO). © 2013 IEEE