An Advanced Deep Learning Framework for Ischemic and Hemorrhagic Brain Stroke Diagnosis Using Computed Tomography (CT) Images
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
Brain stroke is one of the leading causes of mortality and long-term
disability worldwide, highlighting the need for precise and fast prediction
techniques. Computed Tomography (CT) scan is considered one of the most
effective methods for diagnosing brain strokes. The majority of stroke
classification techniques rely on a single slice-level prediction mechanism,
allowing the radiologist to manually choose the most critical CT slice from the
original CT volume. Although clinical evaluations are often used in traditional
diagnostic procedures, machine learning (ML) has opened up new avenues for
improving stroke diagnosis. To supplement traditional diagnostic techniques,
this study investigates the use of machine learning models, specifically
concerning the prediction of brain stroke at an early stage utilizing CT scan
images. In this research, we proposed a novel approach to brain stroke
detection leveraging machine learning techniques, focusing on optimizing
classification performance with pre-trained deep learning models and advanced
optimization strategies. Pre-trained models, including DenseNet201,
InceptionV3, MobileNetV2, ResNet50, and Xception, are utilized for feature
extraction. Additionally, we employed feature engineering techniques, including
BFO, PCA, and LDA, to enhance models' performance further. These features are
subsequently classified using machine learning algorithms such as SVC, RF, XGB,
DT, LR, KNN, and GNB. Our experiments demonstrate that the combination of
MobileNetV2, LDA, and SVC achieved the highest classification accuracy of
97.93%, significantly outperforming other model-optimizer-classifier
combinations. The results underline the effectiveness of integrating
lightweight pre-trained models with robust optimization and classification
techniques for brain stroke diagnosis.
