Texture-based detection of lung pathology in chest radiographs using local binary patterns

College

College of Computer Studies

Department/Unit

Computer Science

Document Type

Conference Proceeding

Source Title

BMEiCON 2015 - 8th Biomedical Engineering International Conference

Publication Date

2-4-2016

Abstract

This paper presents a method that employs texture-based feature extraction and Support Vector Machines (SVM) to classify chest abnormal radiograph patterns namely pleural effusion, pnuemothorax, cardiomegaly and hyperaeration. A similar previous attempt prototyped the classification system that achieved 97% and 87.5% accuracy for pleural effusion and pneumothorax using histogram values, while attaining 70% and 73.33% for cardiomegaly and hyperaeration using image processing schemes. In this work, we aimed to increase the performance in classifying the said lung patterns, specifically for cardiomegaly and hyperaeration. Using texture-based features, the developed system was able to achieve accuracies of 96% and 99% with sensitivities of 97% and 100% for the cardiomegaly and hyperaeration cases, respectively. © 2015 IEEE.

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Digitial Object Identifier (DOI)

10.1109/BMEiCON.2015.7399551

Disciplines

Analytical, Diagnostic and Therapeutic Techniques and Equipment | Computer Sciences

Keywords

Heart—Hypertrophy; Heart—Dilatation; Chest—Radiography; Diagnosis—Data processing; Support vector machines; Binary system (Mathematics)

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