Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt.
Artif Intell Med. 2020 Jan;102:101779. doi: 10.1016/j.artmed.2019.101779. Epub 2019 Dec 10.
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.
癌症是心血管疾病之后的第二大死亡原因。在所有类型的癌症中，脑癌的存活率最低。脑瘤可以根据其形状、质地和位置分为不同类型。正确诊断肿瘤类型可以使医生做出正确的治疗选择，帮助挽救患者的生命。人工智能领域非常需要计算机辅助诊断 (CAD) 系统来帮助医生和放射科医生诊断和分类肿瘤。近年来，深度学习在计算机视觉系统中表现出了乐观的性能。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。我们在一个包含 3 种脑肿瘤类型（脑膜瘤、神经胶质瘤和垂体瘤）的 3064 个 MRI 图像的基准数据集上评估了所提出的模型。我们实现了最高 99%的准确率，优于同一数据集上的其他先前工作。
