Abstract
Magnetic resonance imaging (MRI) is a useful method for diagnosis of tumours in human brain. In this work, MRI images have been analysed to detect the regions containing tumour and classify these regions into three different tumour categories: meningioma, glioma, and pituitary. Deep learning is a relatively recent and powerful method for image classification tasks. Therefore, faster Region-based Convolutional Neural Networks (faster R-CNN), a deep learning method, has been utilized and implemented via TensorFlow library in this study. A publicly available dataset containing 3,064 MRI brain images (708 meningioma, 1426 glioma, 930 pituitary) of 233 patients has been used for training and testing of the classifier. It has been shown that faster R-CNN method can yield an accuracy of 91.66% which is higher than the related work using the same dataset.
Highlights
Cancer is one of the major causes of death today
According to the reports of World Health Organization (WHO), it is estimated that 9.6 million people worldwide died of cancer in 2018. 3050% of these were preventable with early diagnosis
It is estimated that 17,760 adults will die from brain tumours in 2019 [1]
Summary
Cancer is one of the major causes of death today. According to the reports of World Health Organization (WHO), it is estimated that 9.6 million people worldwide died of cancer in 2018 (https://www.who.int/cancer/en/). 3050% of these were preventable with early diagnosis. It is estimated that 17,760 adults will die from brain tumours in 2019 [1]. MRI, a useful method for obtaining high quality brain images, is widely utilized for tumour diagnosis. The purpose of pooling step is to simplify the output by performing nonlinear downsampling, and reducing the number of parameters that the network needs to learn about. These three operations are repeated over tens or hundreds of layers, with each layer learning to detect different features. All two-dimensional arrays are transformed into one single linear vector Such a process is needed for fully connected layers to be used after convolutional layers. The procedure is finished with application of softmax function to provide the final classification output
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