Abstract
Brain tumor detection can make the difference between life and death. Recently, deep learning-based brain tumor detection techniques have gained attention due to their higher performance. However, obtaining the expected performance of such deep learning-based systems requires large amounts of classified images to train the deep models. Obtaining such data is usually boring, time-consuming, and can easily be exposed to human mistakes which hinder the utilization of such deep learning approaches. This paper introduces a novel framework for brain tumor detection and classification. The basic idea is to generate a large synthetic MRI images dataset that reflects the typical pattern of the brain MRI images from a small class-unbalanced collected dataset. The resulted dataset is then used for training a deep model for detection and classification. Specifically, we employ two types of deep models. The first model is a generative model to capture the distribution of the important features in a set of small class-unbalanced brain MRI images. Then by using this distribution, the generative model can synthesize any number of brain MRI images for each class. Hence, the system can automatically convert a small unbalanced dataset to a larger balanced one. The second model is the classifier that is trained using the large balanced dataset to detect brain tumors in MRI images. The proposed framework acquires an overall detection accuracy of 96.88% which highlights the promise of the proposed framework as an accurate low-overhead brain tumor detection system.
Highlights
Classification and grading are presented in [9, 19, 22, 24]. These applications rely on magnetic resonance imaging (MRI) images of the brain, which are better than computed tomography (CT) images because they can provide greater contrast to the soft tissues in the brain compared to CT images
To make this problem computationally tractable these systems usually assume that the important features in MRI images are independent, which limits their ability to capture the relationship associated with the nature between the features, which in turn reduces their accuracy [30]
By forcing the latent space embeddings to follow the normal distribution, the network is forced to fully utilize the latent space so that information is distributed in a way that allows us to sample from any point in the latent space to generate new images that reflect the typical patterns in the original small brain MRI images dataset
Summary
Cancer by its unstable nature remains a curse to humankind [26]. Computeraided diagnosis (CAD) applications are used to assist neurologists. The extracted features are fed into a structured form model to detect and classify brain tumors To make this problem computationally tractable these systems usually assume that the important features in MRI images are independent, which limits their ability to capture the relationship associated with the nature between the features, which in turn reduces their accuracy [30]. On the other hand, automated solutions developed using CNN and its variants have not been able to significantly improve performance This is because CNN and deep learning models, in general, are data-hungry [27]; i.e. In order to achieve the expected good performance, it requires large amounts of training data (classified images). The second model is the classifier that is trained using the large class-balanced dataset to detect brain tumors in MRI images.
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