Design of Brain Tumor Detection System on MRI Image Using CNN

Indira Salsabila Ardan, R. Indraswari
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Abstract

Brain tumor is an abnormal proliferation of brain cells, which may be benign or malignant in nature. Brain cancer, which is frequently diagnosed in individuals of all ages, is a malignant form of a brain tumor and one of the most severe forms of cancer. Each year, an estimated 300 cases of brain tumors, including those in children, are diagnosed in Indonesia. To detect brain tumors, imaging methods such as Magnetic Resonance Imaging (MRI) are utilized. However, radiologists' manual examination of MRI scans might lead to conclusions that differ from one doctor to the next (interobserver error). Research on brain tumor type classification on MRI images is also limited. To identify various types of brain tumors in MRI images, we will therefore construct a system utilizing Convolutional Neural Networks (CNN) and transfer-learning methods. In this study, the Flask framework was successfully used to develop a web-based application to identify distinct form of brain tumors in MRI scans. The model makes use of CNN architecture, a ResNet50V2 base model trained on the ImageNet dataset, a head model with 512 nodes and one entirely connected layer, and an output layer that forecasts the input into four classes of brain MRI images, including “Normal”,”Glioma”, “Meningioma”, and”Pituitary”. Appropriate parameter settings were used to achieve the highest accuracy. In this study, Adam optimization algorithm was used with 60 epochs and a batch size of 32. Additionally, a ten-fold cross-validation technique was implemented. 95% accuracy rate was achieved by implementing the proposed architecture.
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利用 CNN 在核磁共振成像上设计脑肿瘤检测系统
脑肿瘤是脑细胞的异常增殖,性质可能是良性的,也可能是恶性的。脑癌是脑肿瘤的一种恶性形式,也是最严重的癌症之一,经常在各个年龄段的人群中确诊。据估计,印尼每年确诊的脑肿瘤病例有 300 例,其中包括儿童脑肿瘤。为了检测脑肿瘤,需要使用磁共振成像(MRI)等成像方法。然而,放射科医生手动检查核磁共振成像扫描可能会导致不同医生得出不同的结论(观察者之间的误差)。有关核磁共振成像图像上脑肿瘤类型分类的研究也很有限。因此,我们将利用卷积神经网络(CNN)和迁移学习方法构建一个系统,以识别 MRI 图像中的各种脑肿瘤类型。在本研究中,我们成功地利用 Flask 框架开发了一个基于网络的应用程序,用于识别核磁共振成像扫描中不同形式的脑肿瘤。该模型使用了 CNN 架构、在 ImageNet 数据集上训练的 ResNet50V2 基础模型、包含 512 个节点和一个全连接层的头部模型,以及将输入预测为四类脑 MRI 图像(包括 "正常"、"胶质瘤"、"脑膜瘤 "和 "垂体瘤")的输出层。采用适当的参数设置以达到最高准确率。在本研究中,亚当优化算法使用了 60 个历时和 32 个批次。此外,还采用了十倍交叉验证技术。通过实施所提出的架构,准确率达到了 95%。
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