A Robust Computer-Aided Automated Brain Tumor Diagnosis Approach Using PSO-ReliefF Optimized Gaussian and Non-Linear Feature Space

• Published in: Life Vol. 12; no. 12; p. 2036
• Main Authors: Ali, Muhammad Umair, Kallu, Karam Dad, Masood, Haris, Hussain, Shaik Javeed, Ullah, Safee, Byun, Jong Hyuk, Zafar, Amad, Kim, Kawang Su
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06.12.2022; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Brain; Brain cancer; brain MRI; Brain tumors; Classification; Cluster analysis; Clustering; Computational efficiency; Computational neuroscience; Computer-aided medical diagnosis; Computing time; Datasets; Deep learning; Diagnosis; Feature extraction; Glioma; Image enhancement; Image processing; Information retrieval; KAZE; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical imaging; Medical personnel; Meningioma; Methods; Neuroimaging; optimization; Pituitary; Q; ReliefF; ReliefF; optimization; tumor; KAZE; diagnosis; brain MRI; Robustness; Science; Support vector machines; Tomography; tumor; Tumors; Vector quantization; South Korea; diagnosis; KAZE; brain MRI; optimization; tumor; ReliefF
• ISSN: 2075-1729; 2075-1729
• DOI: 10.3390/life12122036

Brain tumors are among the deadliest diseases in the modern world. This study proposes an optimized machine-learning approach for the detection and identification of the type of brain tumor (glioma, meningioma, or pituitary tumor) in brain images recorded using magnetic resonance imaging (MRI). The Gaussian features of the image are extracted using speed-up robust features (SURF), whereas its non-linear features are obtained using KAZE, owing to their high performance against rotation, scaling, and noise problems. To retrieve local-level information, all brain MRI images are segmented into an 8 × 8 pixel grid. To enhance the accuracy and reduce the computational time, the variance-based k-means clustering and PSO-ReliefF algorithms are employed to eliminate the redundant features of the brain MRI images. Finally, the performance of the proposed hybrid optimized feature vector is evaluated using various machine learning classifiers. An accuracy of 96.30% is obtained with 169 features using a support vector machine (SVM). Furthermore, the computational time is also reduced to 1 min compared to the non-optimized features used for training of the SVM. The findings are also compared with previous research, demonstrating that the suggested approach might assist physicians and doctors in the timely detection of brain tumors.