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Multi Graded Brain Tumor Classification using YOLOv7 | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Volume 24, Issue 3, September 2024, Pages 59-76 PDF (1.33 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.24.3.5 | ||
| Authors | ||
| Sarmad Fouad Yaseen* 1; Amjad Jaleel Humaidi2; Ahmed Sabah Al-araji3; Hamed S Al-Raweshidy4 | ||
| 1University of Technology-Iraq | ||
| 2University of Technology-Control and Systems Engineering Department | ||
| 3University of Technology, Iraq | ||
| 4Brunel University, London, UK | ||
| Abstract | ||
| In recent years, significant progress has been made in the field of diagnosis and classification of brain tumors, mainly attributed to advancements in artificial intelligence and medical imaging. The main objective of this study is to improve the detection and classification of brain tumors by applying and utilizing of artificial intelligence (AI) and recent advancements in medical imaging techniques. Automation of the process of tumor identification and then classification, in addition to tumor grading, will definitely improve all procedures of brain tumor treatment and enhance patient care. The proposed system combines convolutional neural networks (CNNs), which act as extract features, and the You Only Look Once Algorithm (YOLOv7) for effective object identification and accurate classification. The methodology described in this study involves employing a technique of multilayer classification, which integrates three distinct datasets. This comprehensive approach shows an exceptional levels of accuracy and precision. At the initial level, the model attains a 99.78% accuracy in distinguishing between tumor and nontumor cases. At the next level the system accurately sorts types of brain tumors (such, as glioma, meningioma and pituitary tumors) with an average accuracy of 99.35%. Moving on to the final stage it successfully distinguishes between low grade and high grade glioma tumors with a precision of 93.07%. Moreover the model shows accuracies ranging from 99.41%, to 99.61% when classifying types of brain tumors and nontumor cases. The proposed system has the ability to determine the boundaries of the tumor, and thus this has helped in calculating the sizes of tumors with high accuracy. | ||
| Keywords | ||
| Brain tumor; MRI; Convolutional Neural Network; YOLO; Tumor grading | ||
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