A CLASSIFIER MODEL FOR X-RAY IMAGE BASED ON DEEP LEARNING TECHNIQUE | ||||
| International Journal of Advanced Scientific Research and Innovation | ||||
| Volume 6, Issue 2, December 2023, Page 109-116 PDF (393.13 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijasri.2024.266603.1014 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Hammam Abdelaal 
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| Department of Information Technology, Faculty of Computers and information, Luxor University, Egypt | ||||
| Abstract | ||||
| Chest infections encompass a wide range of conditions, including pneumonia, tuberculosis, bronchitis, and other respiratory illnesses. By harnessing the power of AI and deep learning algorithms, we aim to provide healthcare professionals with a valuable diagnostic tool. The AI model is trained on an extensive dataset of labeled chest x-ray images, enabling it to learn and recognize patterns indicative of different types of infections. By leveraging convolutional neural networks (CNNs) and advanced image recognition techniques, the model automatically identifies abnormalities and subtle indicators of infection, ensuring accurate and efficient analysis. One of the key advantages of AI model is its ability to rapidly process and analyze a large volume of x-ray images. Additionally, the model accommodates variations in image quality and positioning, making it adaptable to different healthcare settings, after conducting the results of the experiments, the accuracy of this model reached up to 92% | ||||
| Keywords | ||||
| chest disease; x-ray images; deep learning; CNNs; feature extraction; segmentation; cross-validation | ||||
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