Feature Fusion-based Brain Stroke Identification Model Using Computed Tomography Images

Abstract

Accurate and rapid diagnosis is essential in the healthcare system for the detection of strokes to mitigate the devastating effects. This study introduces an innovative model for identifying strokes using advanced deep learning (DL) architectures, including SqueezeNet v1.1 and MobileNet V3-Small, feature fusion approaches, and CatBoost models. Using SqueezeNet v1.1 and MobileNet V3-Small, the authors extract meaningful features from computed tomography images that capture local details and global patterns suggesting stroke conditions. Subsequently, they employ feature fusion to combine the complementary representations derived by both architectures, consequently boosting the discriminative capability of the feature set. The Optuna-based CatBoost model is employed to predict stroke using the fused features. The experimental findings show outstanding performance, with a considerable accuracy of 99.1%. The high accuracy level demonstrates our suggested method’s effectiveness in precisely detecting strokes from medical imaging data. Combining DL architectures, feature fusion, and gradient-boosting models offers a promising approach to enhancing stroke diagnosis systems. This can potentially improve patient outcomes and clinical decision-making in stroke treatment.