Br35H :: Brain Tumor Detection 2020

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties.Application of automated classification techniques using Machine Learning(ML) and Artificial Intelligence(AI)has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using Convolution-Neural Network (CNN), Artificial Neural Network (ANN), and Transfer-Learning (TL) would be helpful to doctors all around the world.

脑肿瘤是儿童与成人群体中极具侵袭性的疾病之一。脑肿瘤占所有原发性中枢神经系统（Central Nervous System，CNS）肿瘤的85%至90%。每年约有11700人被确诊为脑肿瘤患者。脑部恶性肿瘤或中枢神经系统肿瘤患者的5年生存率，男性约为34%，女性约为36%。脑肿瘤可分为良性肿瘤、恶性肿瘤、垂体瘤等类型。为提升患者的预期寿命，需采取规范的治疗方案、诊疗规划与精准诊断手段。检测脑肿瘤的最优技术为磁共振成像（Magnetic Resonance Imaging，简称MRI）。扫描过程会产生海量图像数据，此类图像需由放射科医师人工阅片。由于脑肿瘤及其病变特征的复杂性，人工阅片极易出现误差。基于机器学习（Machine Learning，简称ML）与人工智能（Artificial Intelligence，简称AI）的自动化分类技术，其准确率始终高于人工分类方式。因此，本研究提出采用卷积神经网络（Convolution-Neural Network，简称CNN）、人工神经网络（Artificial Neural Network，简称ANN）以及迁移学习（Transfer-Learning，简称TL）等深度学习算法，实现脑肿瘤的检测与分类，该系统将为全球临床医师提供有效辅助。

搜集汇总

背景与挑战

背景概述

Br35H :: Brain Tumor Detection 2020是一个包含801张标注脑部MRI图像的数据集，分为训练、验证和测试集，旨在通过深度学习技术提高脑肿瘤检测的自动化分类准确性。