Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays
Our research focused on creating an advanced machine-learning algorithm that accurately detects anomalies in chest X-ray images to provide healthcare professionals with a reliable tool for diagnosing various lung conditions. To achieve this, we analysed a vast collection of X-ray images and utilised...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/18/2979 |
| _version_ | 1797580628160413696 |
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| author | Zaid Mustafa Heba Nsour |
| author_facet | Zaid Mustafa Heba Nsour |
| author_sort | Zaid Mustafa |
| collection | DOAJ |
| description | Our research focused on creating an advanced machine-learning algorithm that accurately detects anomalies in chest X-ray images to provide healthcare professionals with a reliable tool for diagnosing various lung conditions. To achieve this, we analysed a vast collection of X-ray images and utilised sophisticated visual analysis techniques; such as deep learning (DL) algorithms, object recognition, and categorisation models. To create our model, we used a large training dataset of chest X-rays, which provided valuable information for visualising and categorising abnormalities. We also utilised various data augmentation methods; such as scaling, rotation, and imitation; to increase the diversity of images used for training. We adopted the widely used You Only Look Once (YOLO) v8 algorithm, an object recognition paradigm that has demonstrated positive outcomes in computer vision applications, and modified it to classify X-ray images into distinct categories; such as respiratory infections, tuberculosis (TB), and lung nodules. It was particularly effective in identifying unique and crucial outcomes that may, otherwise, be difficult to detect using traditional diagnostic methods. Our findings demonstrate that healthcare practitioners can reliably use machine learning (ML) algorithms to diagnose various lung disorders with greater accuracy and efficiency. |
| first_indexed | 2024-03-10T22:52:28Z |
| format | Article |
| id | doaj.art-bcc8aa82bb914b5cbe391885231be92a |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T22:52:28Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-bcc8aa82bb914b5cbe391885231be92a2023-11-19T10:14:13ZengMDPI AGDiagnostics2075-44182023-09-011318297910.3390/diagnostics13182979Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-raysZaid Mustafa0Heba Nsour1Department of Computer Information Systems, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt 19117, JordanDepartment of Computer Science, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt 19117, JordanOur research focused on creating an advanced machine-learning algorithm that accurately detects anomalies in chest X-ray images to provide healthcare professionals with a reliable tool for diagnosing various lung conditions. To achieve this, we analysed a vast collection of X-ray images and utilised sophisticated visual analysis techniques; such as deep learning (DL) algorithms, object recognition, and categorisation models. To create our model, we used a large training dataset of chest X-rays, which provided valuable information for visualising and categorising abnormalities. We also utilised various data augmentation methods; such as scaling, rotation, and imitation; to increase the diversity of images used for training. We adopted the widely used You Only Look Once (YOLO) v8 algorithm, an object recognition paradigm that has demonstrated positive outcomes in computer vision applications, and modified it to classify X-ray images into distinct categories; such as respiratory infections, tuberculosis (TB), and lung nodules. It was particularly effective in identifying unique and crucial outcomes that may, otherwise, be difficult to detect using traditional diagnostic methods. Our findings demonstrate that healthcare practitioners can reliably use machine learning (ML) algorithms to diagnose various lung disorders with greater accuracy and efficiency.https://www.mdpi.com/2075-4418/13/18/2979abnormalitiesmachine learningimage processingimage classificationCADmagnetic resonance imaging |
| spellingShingle | Zaid Mustafa Heba Nsour Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays Diagnostics abnormalities machine learning image processing image classification CAD magnetic resonance imaging |
| title | Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays |
| title_full | Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays |
| title_fullStr | Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays |
| title_full_unstemmed | Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays |
| title_short | Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays |
| title_sort | using computer vision techniques to automatically detect abnormalities in chest x rays |
| topic | abnormalities machine learning image processing image classification CAD magnetic resonance imaging |
| url | https://www.mdpi.com/2075-4418/13/18/2979 |
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