RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays
<b>Background:</b> Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/13/1728 |
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| author | Hanan Aljuaid Hessa Albalahad Walaa Alshuaibi Shahad Almutairi Tahani Hamad Aljohani Nazar Hussain Farah Mohammad |
| author_facet | Hanan Aljuaid Hessa Albalahad Walaa Alshuaibi Shahad Almutairi Tahani Hamad Aljohani Nazar Hussain Farah Mohammad |
| author_sort | Hanan Aljuaid |
| collection | DOAJ |
| description | <b>Background:</b> Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpretations and delays in treatment. <b>Method:</b> The purpose of this research is the development of a RadAI model. The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. Moreover, deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable potential in automating medical image analysis, including chest X-rays. This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. These models are compared based on accuracy, precision, recall, and F1-score. <b>Results:</b> The outstanding performance of RadAI shows its potential to assist radiologists to interpret the detected chest abnormalities accurately. <b>Conclusions:</b> RadAI is beneficial in enhancing the accuracy and efficiency of chest X-ray interpretation, ultimately supporting the timely and reliable diagnosis of lung abnormalities. |
| format | Article |
| id | doaj-art-c169b256843043e39ba9ef6d4f2275f3 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-c169b256843043e39ba9ef6d4f2275f32025-08-20T03:16:42ZengMDPI AGDiagnostics2075-44182025-07-011513172810.3390/diagnostics15131728RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-RaysHanan Aljuaid0Hessa Albalahad1Walaa Alshuaibi2Shahad Almutairi3Tahani Hamad Aljohani4Nazar Hussain5Farah Mohammad6Computer Science Department, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi ArabiaResearch Chair of AI in Healthcare, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi ArabiaResearch Chair of AI in Healthcare, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi ArabiaResearch Chair of AI in Healthcare, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi ArabiaResearch Chair of AI in Healthcare, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi ArabiaResearch Chair of AI in Healthcare, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi ArabiaResearch Chair of AI in Healthcare, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia<b>Background:</b> Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpretations and delays in treatment. <b>Method:</b> The purpose of this research is the development of a RadAI model. The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. Moreover, deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable potential in automating medical image analysis, including chest X-rays. This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. These models are compared based on accuracy, precision, recall, and F1-score. <b>Results:</b> The outstanding performance of RadAI shows its potential to assist radiologists to interpret the detected chest abnormalities accurately. <b>Conclusions:</b> RadAI is beneficial in enhancing the accuracy and efficiency of chest X-ray interpretation, ultimately supporting the timely and reliable diagnosis of lung abnormalities.https://www.mdpi.com/2075-4418/15/13/1728diagnosechest X-raydeep learningimage classificationconvolutional neural networks (CNNs)King Abdullah University Hospital (KAAUH) |
| spellingShingle | Hanan Aljuaid Hessa Albalahad Walaa Alshuaibi Shahad Almutairi Tahani Hamad Aljohani Nazar Hussain Farah Mohammad RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays Diagnostics diagnose chest X-ray deep learning image classification convolutional neural networks (CNNs) King Abdullah University Hospital (KAAUH) |
| title | RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays |
| title_full | RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays |
| title_fullStr | RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays |
| title_full_unstemmed | RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays |
| title_short | RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays |
| title_sort | radai a deep learning based classification of lung abnormalities in chest x rays |
| topic | diagnose chest X-ray deep learning image classification convolutional neural networks (CNNs) King Abdullah University Hospital (KAAUH) |
| url | https://www.mdpi.com/2075-4418/15/13/1728 |
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