A Hybrid Deep Learning Model for Brain Tumour Classification

Abstract

A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

“…This mere modulation of standard Convolutional filters by GF allows the presented structure to study comparatively small feature maps reducing the demand for network parameters. Rasool et al [12] presented a novel hybrid CNN-oriented structure for classifying 3 BT types by using MRI images. The technique recommended in this study will use hybrid DL classification related to CNN with 2 techniques.…”

Brain Tumor Image Processing Using Fine-Tuned Resnet-101 Classification Model

“…If there are more components per data point than there are training data samples, both the CNN and the SVM will lose poorly. [14]. The size of the input image is fixed and cannot be increased owing to memory constraints, which is one of the 3D drawbacks network [15].…”

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

“…An ensemble deep learning-based system was designed by Rasool et al [ 20 ] for the categorization of three different kinds of brain tumors. The authors used the ensemble deep learning model with fine-tuned GoogleNet and achieved an accuracy of 93.1%.…”