A Two Phase Hybrid Classifier based on Structure Similarities and Textural Features for Accurate Meningioma Classification

Meningioma subtype classification is a complex pattern classification problem of digital pathology due to heterogeneity issues of tumor texture, low inter-class and high intra-class texture variations of tumor samples, and architectural variations of cellular components. The basic aim is the achievement of significantly high classification results for all the subtypes of meningioma while dealing with inherent complexity and texture variations. The ultimate goal is to mimic the prognosis decision of expert pathologists and assist newer pathologists in making right and quick decisions. In this paper, a novel hybrid classification framework based on nuclei shape matching and texture analysis is proposed for classification of four subtypes of grade-I benign meningioma. Meningothelial and fibroblastic subtypes are classified on basis of nuclei shape matching through skeletons and shock graphs while an optimized texture-based evolutionary framework is designed for the classification of transitional and psammomatous subtypes. Classifier-based evolutionary feature selection is performed using Genetic Algorithm (GA) in combination with Support Vector Machine (SVM) to select the optimal combination of higher-order statistical features extracted from morphologically processed RGB color channel images. The proposed hybrid classifier employed leave-one-patient-out 5-fold cross validation and achieved an overall 95.63% mean classification accuracy. Keywords—Meningioma; Computer-Aided Diagnosis; Brain Tumour Classification; Cell Segmentation; Shape Analysis; Texture Analysis