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A Comprehensive Review on Brain Tumour Classification through Deep Learning Utilizing Convolutional Neural Networks

Sohan Lal Gupta, Vikram Khadelwal, Nimish Arvind, Rajesh Kumar, Girraj Khadelwal


Abstract- Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. Brain tumours are classified using a biopsy, which is not normally done before conclusive brain surgery. The enhancement of this technology by machine learning could aid radiologists in tumour detection without the use of invasive procedures. The convolutional neural network (CNN) is a machine-learning-based algorithm that has achieved meaningful results in image segmentation and classification. Pattern and image recognition problems commonly employ convolutional neural networks (CNNs). Automatic brain tumour classification is a tough challenge due to the significant spatial and structural diversity of the brain tumour's surrounding region. Convolutional Neural Networks (CNN) are capable of performing this function with simplicity. In this paper, we have performed a comparative study of different tumour classification methods using CNN.


Brain-tumour, Deep-learning, convolutional neural network, image-classification, MRI imaging

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