Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.

癌症是仅次于心血管疾病的第二大死亡原因。在所有类型的癌症中，脑癌的存活率最低。脑瘤的形状，质地和位置可能不同，其类型也不同。对肿瘤类型的正确诊断使医生能够做出正确的治疗选择，并有助于挽救患者的生命。在人工智能领域，迫切需要一种计算机辅助诊断（CAD）系统，以帮助医生和放射线医师进行肿瘤的诊断和分类。近年来，深度学习在计算机视觉系统中显示出乐观的表现。在本文中，我们提出了一种使用残差网络对脑肿瘤类型进行分类的增强方法。我们在基准数据集上评估提出的模型，该基准数据集包含3种脑肿瘤类型（脑膜瘤，胶质瘤和垂体瘤）的3064张MRI图像。我们在同一数据集上取得了99％的最高精度，胜过其他先前的工作。