Texture-based detection of lung pathology in chest radiographs using local binary patterns
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 achi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | text |
| Published: |
Animo Repository
2016
|
| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/2513 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3512/type/native/viewcontent |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | De La Salle University |
| Summary: | 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. |
|---|