Exemplar deep and hand-modeled features based automated and accurate cerebral hemorrhage classification method

Abstract

Background: Cerebral hemorrhage (CH) is a commonly seen disease, and an accurate diagnosis of the type of CH is a very crucial step in treatment. Therefore, CH requires a prompt and accurate diagnosis. To simplify this process, an accurate CH classification model is presented using a machine learning technique.

Material and method: A computed tomography (CT) image dataset was collected retrospectively in this research. This dataset contains 9818 images with five categories. An exemplar fused feature generator is presented to classify these features. This generator uses pre-trained AlexNet, local binary pattern (LBP), and local phase quantization (LPQ). The neighborhood component analysis (NCA) method selects the top features, and the chosen feature vector is classified on the support vector machine.

Results: Six validation methods are utilized to calculate the performance of the presented exemplar fused features and NCA-based CH classification model. This model attained 97.47%, 96.05%, 95.21%, 93.62%, 91.28% and 96.34% accuracies using five hold-out validations and ten-fold cross-validation respectively.

Conclusions: The calculated results clearly demonstrate the success and robustness of the introduced exemplar fused feature generation and NCA-based model. Furthermore, this model can be used in emergency services to overcome a prompt diagnosis of CH.

Keywords: Cerebral hemorrhage identification; Exemplar fused feature generation; Hand-modeled feature extraction; Smart health assistant; Transfer learning.