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. 2022 May 18:2022:8330833.
doi: 10.1155/2022/8330833. eCollection 2022.

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

Ebrahim Mohammed Senan  1 , Mukti E Jadhav  2 , Taha H Rassem  3 , Abdulaziz Salamah Aljaloud  4 , Badiea Abdulkarem Mohammed  4 , Zeyad Ghaleb Al-Mekhlafi  4
Affiliations

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

Ebrahim Mohammed Senan et al. Comput Math Methods Med. .

Abstract

Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients' chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.

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Conflict of interest statement

The authors declare no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
General structure of the combination of deep and machine learning techniques.
Figure 2
Figure 2
Samples of a dataset of an MRI of brain tumours. Source: (https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri).
Figure 3
Figure 3
Samples of the dataset after the enhancement process.
Figure 4
Figure 4
AlexNet architecture.
Figure 5
Figure 5
ResNet-18 architecture.
Figure 6
Figure 6
Hybrid architecture between deep and machine learning: (a) AlexNet+SVM; (b) ResNet-18+SVM.
Figure 7
Figure 7
A set of image samples after applying data augmentation.
Figure 8
Figure 8
Training and loss process of the ResNet-18 model.
Figure 9
Figure 9
(a) Confusion matrix for AlexNet to evaluate MRI brain tumours. (b) Confusion matrix for ResNet-18 to evaluate MRI brain tumours.
Figure 10
Figure 10
(a) Confusion matrix for AlexNet+SVM to evaluate MRI brain tumours. (b) Confusion matrix for ResNet-18+SVM to evaluate MRI brain tumours.
Figure 11
Figure 11
Performance of the proposed systems for the brain tumour dataset.
Figure 12
Figure 12
Performance of the four models for the detection of each brain tumour.
Figure 13
Figure 13
Performance comparison of the proposed systems with related studies.
See this image and copyright information in PMC

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