Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning

Abstract

Brain tumors are common tumors arising from parenchymal cells in the brain and the membranes that surround the brain. The most common brain tumors are glioma and meningioma. They can be benign or malignant. Treatment modalities such as surgery and radiotherapy are applied in malignant tumors. Tumors may be very small in the early stages and may be missed by showing findings similar to normal brain parenchyma. The correct determination of the localization of the tumor and its neighborhood with the surrounding vital tissues contributes to the determination of the treatment algorithm. In this paper, we aim to determine the classification and localization of gliomas originating from the parenchymal cells of the brain and meningiomas originating from the membranes surrounding the brain in brain magnetic resonance images using artificial intelligence methods. At first, the two classes of meningioma and glioma tumors of interest are selected in a public dataset. Relevant tumors are then labeled with the object labeling tool. The resulting labeled data is passed through the EfficientNet for feature extraction. Then Path Aggregation Network (PANet) is examined to generate the feature pyramid. Finally, object detection is performed using the detection layer of the You Only Look Once (YOLO) algorithm. The performance of the suggested method is shown with precision, recall and mean Average Precision (mAP) performance metrics. The values obtained are 0.885, 1.0, and 0.856, respectively. In the presented study, meningioma, and glioma, are automatically detected. The results demonstrate that using the proposed method will benefit medical people.