Highlights
- This paper presents a novel method to categorize MRI images into normal and abnormal groups by WE, support vector machin,e and CBSSO optimization method.
- The efficiency of this approach is confirmed through its comparison with several other methods (BP network, kernel SVM and PSO-KSVM).
- The WE is also authenticated as an efficient characteristic in the categorization of MRI images.
Abstract
Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes.