Abstract
The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.
Highlights
Cancer is the second leading cause of death globally, according to the World Health Organization (WHO) [1]
Meningiomas arise from the membranes that cover the brain and surround the central nervous system, whereas pituitary tumors are lumps that sit inside the skull [3,4,5,6]
We present a new convolutional neural network (CNN) architecture for brain tumor classification of three tumor types: meningioma, glioma, and pituitary tumor from T1-weighted contrast-enhanced magnetic types: meningioma, glioma, and pituitary tumor from T1-weighted contrast-enhanced magnetic resonance images
Summary
Cancer is the second leading cause of death globally, according to the World Health Organization (WHO) [1]. A tumor could be benign, pre-carcinoma, or malign. Benign tumors differ from malign in that benign generally do not spread to other organs and tissues and can be surgically removed [2]. Some of the primary brain tumors are gliomas, meningiomas, and pituitary tumors. Gliomas are a general term for tumors that arise from brain tissues other than nerve cells and blood vessels. Meningiomas arise from the membranes that cover the brain and surround the central nervous system, whereas pituitary tumors are lumps that sit inside the skull [3,4,5,6]. The most important difference between these three types of tumors is that meningiomas are typically benign, and gliomas are most commonly malignant. Even if benign, can cause other medical damage, unlike meningiomas, which are slow-growing tumors [5,6]. Because of the information mentioned above, the precise differentiation between these three types of tumors represents a very important step of the clinical diagnostic process and later effective assessment of patients
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