Abstract
Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from the gridding phenomenon. An incremental, even number dilation rate takes advantage of the reduced computational overhead and also overcomes the adverse effects of gridding. Comparative analysis between different combinations of dilation rates for the different convolution layers, help validate the results. The computational overhead in terms of efficiency for training the model to reach an acceptable threshold accuracy of 90% is another parameter to compare the model performance.
Highlights
Tumors are a mass of abnormal tissue that ascends without palpable cause from body cells and have no crucial function
We have proposed the classic problem of detecting tumors from MRI images using a dilated deep convolutional neural network (CNN)
We have bench-marked the performance of the proposed model with those of existing models such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)
Summary
Tumors are a mass of abnormal tissue that ascends without palpable cause from body cells and have no crucial function. Brain tumor detection at the early stage and availing proper treatment can save the patient from any adverse damage to the brain [1]. Computer-assisted techniques such as using deep learning for feature extraction, and classification techniques are being used intensively to diagnose the patients’ brains to check if there are any tumors. Especially convolutional neural networks, have been proposed in recent years [1,2,3,4,5,6] these proposed techniques have failed to acquire high accuracy. There is a need to develop new techniques for the detection of brain tumor. We have proposed the classic problem of detecting tumors from MRI images using a dilated deep convolutional neural network (CNN). We have bench-marked the performance of the proposed model with those of existing models such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)
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