Detection of Orbital Tumors on MRI images using Convolutional Neural Networks | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Volume 23, Issue 3, September 2023, Page 9-18 PDF (772.99 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2023.189590.1250 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Esraa Ahmed Nabeeh Negm ElDin Allam   1; Marco Alfonse 1; Abdel-Badeeh M. Salem 2
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| 1Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt | ||||
| 2Computer Sciece Department, Faculty of Computer and Information Sciences, Ain Shams University | ||||
| Abstract | ||||
| Orbital tumors are the most common type of tumor affecting the orbit. Some factors, such as technical causes relating to imaging quality and human error, contribute to radiologists misdiagnosing eye tumors. Computer-aided detection systems (CADs) are being developed to address these limitations and have recently been used in numerous imaging modalities for eye tumor diagnosis. CAD technologies increase radiologists' ability to detect and distinguish between normal and diseased tissues. These techniques are only conducted as a second opinion, but the radiologist makes the final decisions. This article presents the contemporary CAD method for detecting orbital tumors on magnetic resonance imaging (MRI) utilizing Convolutional Neural Networks (CNN). Pre-processing, Data Augmentation, Classification, and Evaluation are the four stages that involve our CAD system. Two datasets were used for MRI images: 1404 MRI T1-weighted images and 1560 MRI T2-weighted images. The system was evaluated by many evaluation metrics including the recognition rate which gives 95% for T1-weighted images and 94% for T2-weighted images. | ||||
| Keywords | ||||
| Artificial Intelligence; Deep Learning; Orbital Tumor; Image Classification; Medical Informatics | ||||
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