Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

• Published in: Life (Basel, Switzerland) Vol. 12; no. 7; p. 1084
• Main Authors: Almalki, Yassir Edrees, Ali, Muhammad Umair, Ahmed, Waqas, Kallu, Karam Dad, Zafar, Amad, Alduraibi, Sharifa Khalid, Irfan, Muhammad, Basha, Mohammad Abd Alkhalik, Alshamrani, Hassan A., Alduraibi, Alaa Khalid
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 20.07.2022; MDPI
• Subjects: Accuracy; Biopsy; Brain; Brain cancer; brain tumor; Brain tumors; Classification; Computational neuroscience; Computing time; Datasets; Deep learning; Diagnosis; Feature extraction; Glioma; Histograms; Image processing; Learning algorithms; Life expectancy; Life span; Machine learning; Magnetic resonance imaging; magnetic resonance imaging (MRI); Medical imaging; Memory; Meningioma; Neuroimaging; Pituitary; Support vector machines; Tomography; Tumors
• ISSN: 2075-1729; 2075-1729
• DOI: 10.3390/life12071084

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.