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Accurate detection of brain tumors in MRI images using the YOLO algorithm
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Behnam Solatinia *   , Amirhesan Yahyapour |
| Department of Genetics, Environmental Sciences Research Institute, Graduate University of Advanced Technology, Kerman, Iran , behnamsowlati@gmail.com |
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Abstract: (59 Views) |
Introduction: Accurate and early detection of brain tumors from MRI images plays a vital role in improving prognosis and treatment planning for patients. However, manual interpretation of MRI images is time-consuming and prone to human error. This study aims to present and evaluate an automated and efficient method based on the YOLOv8 algorithm and deep learning for detecting brain tumors in MRI images. Materials and Methods: In this study, the YOLOv8 model was trained using a dataset of 500 labeled brain tumor MRI images. The model's performance on the evaluation and test datasets was assessed using the metrics of Precision, Recall, and AP50. The model was trained in the Google Colab environment using a Tesla T4 GPU and over 100 epochs. Results: The results showed that the trained YOLOv8 algorithm achieved high accuracy and speed in detecting brain tumors. The obtained AP50 values on the evaluation (94.5) and test (94.6) datasets, along with high Precision and Recall values, were evidence of the model's strong and stable performance in tumor detection. The qualitative examination of sample images also confirmed the high accuracy of the algorithm in locating and detecting tumors. Furthermore, the short training time and high detection speed were other advantages of the YOLOv8 algorithm in this study. Conclusion: The present study demonstrates that the YOLOv8 algorithm has significant potential for the automatic and efficient detection of brain tumors in MRI images. The appropriate balance between accuracy and speed, as well as the good generalizability of the model, makes YOLOv8 a valuable auxiliary tool for radiologists achieving faster and more accurate detection of brain tumors. Future studies focus on increasing the volume of training data, improving the model architecture, and clinically evaluating the proposed approach.
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| Keywords: Brain Neoplasms, Tomography, Early Diagnosis, Prognosis, Artificial Intelligence |
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Full-Text [PDF 1599 kb]
(12 Downloads)
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Type of Study: Research --- Open Access, CC-BY-NC |
Subject:
Bioinformatics in Neuroscience
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