Glioma Tumors' Classification Using Deep-Neural-Network-Based Features with SVM Classifier

• Published in: Diagnostics (Basel) Vol. 12; no. 4; p. 1018
• Main Authors: Latif, Ghazanfar, Ben Brahim, Ghassen, Iskandar, D N F Awang, Bashar, Abul, Alghazo, Jaafar
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18-04-2022; MDPI
• Subjects: Accuracy; Automation; Brain cancer; Brain research; Classification; CNN features; convolutional neural networks; Datasets; Literature reviews; Magnetic resonance imaging; Medical research; multi-class Glioma tumors; Support vector machines; Tomography; tumor classification; Tumors; Wavelet transforms; multi-class Glioma tumors; tumor classification; convolutional neural networks; CNN features
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12041018

The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient's life. This has increased the efforts to automate the detection and diagnosis process without human intervention, allowing the detection of multiple types of tumors from MR images. This research paper proposes a multi-class Glioma tumor classification technique using the proposed deep-learning-based features with the Support Vector Machine (SVM) classifier. A deep convolution neural network is used to extract features of the MR images, which are then fed to an SVM classifier. With the proposed technique, a 96.19% accuracy was achieved for the HGG Glioma type while considering the FLAIR modality and a 95.46% for the LGG Glioma tumor type while considering the T2 modality for the classification of four Glioma classes (Edema, Necrosis, Enhancing, and Non-enhancing). The accuracies achieved using the proposed method were higher than those reported by similar methods in the extant literature using the same BraTS dataset. In addition, the accuracy results obtained in this work are better than those achieved by the GoogleNet and LeNet pre-trained models on the same dataset.