Stroke Classification in Brain CT Images Using Transfer Learning With Fastai.
Authors
Affiliations (3)
Affiliations (3)
- Dr. Bajaj is with Applied Sciences (Computer Applications), I.K. Gujral Punjab Technical University, Kapurthala, Punjab, India.
- Dr. Bala is with Department of Computer Science and Engineering, Khalsa College of Engineering and Technology, Amritsar, Punjab, India.
- Dr. Angurala is with Department of Computer Science, Guru Nanak Dev University College, Pathankot, Punjab, India.
Abstract
To identify strokes, which rank among the leading global causes of death, doctors commonly employ computed tomography (CT) imaging. Researchers are increasingly applying deep learning (DL) and machine learning (ML) methods to improve the accuracy of stroke classification and detection. In particular, DL methods have demonstrated significant potential in segmenting stroke regions within brain medical images. However, training convolutional neural networks from scratch is challenging due to dataset size constraints. Achieving timely and precise stroke diagnosis remains a significant hurdle. The purpose of this research is to classify brain stroke cases using CT scan images through transfer learning strategies. Pretrained architectures including ResNet, VGG, Inception, and EfficientNet are utilized, with optimization carried out in the fastai framework to improve computational efficiency and reduce training time. We developed these artifical intelligence (AI) systems by training them with data, making adjustments throughout the process to ultimately categorize patients as having a stroke or not. We evaluated different models using measures such as accuracy, speed, and how well they differentiate normal and stroke-affected CT scans of the brain. This helped us choose the most effective model. In this research, ResNet50 outperformed other models in accuracy (88%), and DCNN was the most time-efficient model.