Madona B. Sahaai*, G. R. Jothilakshmi, E. Praveen, V. Hemath Kumar
Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1507-1521, 2023, DOI:10.32604/iasc.2023.032256
Abstract Magnetic Resonance Imaging (MRI) is one of the important resources for identifying abnormalities in the human brain. This work proposes an effective Multi-Class Classification (MCC) system using Binary Robust Invariant Scalable Keypoints (BRISK) as texture descriptors for effective classification. At first, the potential Region Of Interests (ROIs) are detected using features from the accelerated segment test algorithm. Then, non-maxima suppression is employed in scale space based on the information in the ROIs. The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour (kNN), Support Vector Machine (SVM) and Random Forest (RF). An MCC system… More >
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