Image processing concepts can visualize the different anatomy structure of the human body. Recent advancements in the field of deep learning have made it possible to detect the growth of cancerous tissue just by a patient's brain Magnetic Resonance Imaging (MRI) scans. These methods require very high accuracy and meager false negative rates to be of any practical use. This paper presents a Convolutional Neural Network (CNN) based transfer learning approach to classify the brain MRI scans into two classes using three pre-trained models. The performances of these models are compared with each other. Experimental results show that the Resnet-50 model achieves the highest accuracy and least false negative rates as 95% and zero respectively. It is followed by VGG-16 and Inception-V3 model with an accuracy of 90% and 55% respectively.

翻译：图像处理概念可以想象人体不同的解剖结构。 最近深层学习领域的进步使得能够通过病人的大脑磁共振成像(MRI)扫描来检测癌症组织的增长。 这些方法需要非常高的精度和微小的假负率才能实际使用。 本文展示了基于革命神经网络(CNN)的转移学习方法,用三个预先培训的模型将脑MRI扫描分为两个类别。 这些模型的性能相互比较。 实验结果表明,Resnet- 50模型的精确率最高,而负率最低,分别为95%和零。 随后是VGG-16和Inception-V3模型,精确率分别为90%和55%。