Performance evaluation on brain tumor detection.

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient’s brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.早期精准诊断脑瘤类型，对临床治疗流程至关重要，可挽救全球众多患者的生命。磁共振成像（MRI）扫描具有无创性，可免除患者接受令人不适的活检操作，因此常被用于肿瘤识别与检出。但单例患者的脑部MRI扫描会从多个角度生成大量三维图像，人工识别肿瘤不仅难度极高，还需耗费大量时间。此外，脑瘤在位置、大小与形态上的异质性，进一步增加了肿瘤检测与分型的难度。为此，学界已提出计算机辅助诊断（CAD）系统，用于脑瘤的检测与分类任务。本文提出一款全新的统一端到端深度学习模型TumorDetNet，用于脑瘤的检测与分型。该模型架构搭载48个卷积层，并结合泄漏ReLU（LReLU）与整流线性单元（ReLU）两种激活函数，以提取最具辨识度的深层特征图。此外，模型还引入平均池化层与丢弃（dropout）层，以学习差异化特征并降低过拟合风险。最终，通过一层全连接层与Softmax层，实现脑瘤的检测与多类别分型。我们在卡格尔（Kaggle）平台的6套标准脑部MRI脑瘤数据集上，对所提方法的性能进行了评估，该数据集涵盖脑瘤良恶性（恶性与良性）以及胶质瘤（glioma）、垂体瘤（pituitary）与脑膜瘤（meningioma）的分型任务。实验结果显示，本模型可精准检出脑瘤，整体识别准确率达99.83%；良恶性脑瘤分类准确率可达100%，胶质瘤、垂体瘤与脑膜瘤的分型准确率为99.27%。上述结果充分证明了所提方法在脑瘤可靠检测与分类任务中的优异效能。