Comparative analysis of deep learning techniques for accurate stroke detection

Abstract

Background: The traditional diagnosis of strokes through computed tomography (CT) heavily relies on radiologists’ expertise for accurate interpretation. However, the increasing demand for this critical task exceeds the available radiologist workforce, necessitating innovative solutions. This research addresses this challenge by introducing deep learning techniques to enhance the initial screening of stroke cases, thereby augmenting radiologists’ diagnostic capabilities.

Objective: This study aims to compare four techniques for classifying stroke lesions in CT images.

Materials and methods: Four distinct models-CNN-2-Model, LeNet, GoogleNet, and VGG-16-were trained using a dataset comprising 1,636 CT images, including 1,111 normal brain images and 525 stroke images. Seventy percent of the images were used to train the most effective deep learning model, and subsequently, these images were utilized to evaluate the performance of each model. The evaluation involved assessing accuracy, precision, sensitivity, specificity, F1 score, false positive rate, and AUC.

Results: The evaluation process included a comprehensive statistical analysis of the models’ prediction results. The findings revealed that VGG-16 emerged as the top-performing deep learning model, achieving an impressive accuracy of 0.969, precision of 0.952, sensitivity of 0.952, specificity of 0.978, F1 score of 0.952, false positive rate of 0.022, and AUC of 0.965.

Conclusion: In conclusion, deep learning techniques, particularly the VGG-16 model, demonstrate significant promise in enhancing the accuracy of stroke lesion classification in CT images. These findings underscore the potential of leveraging advanced technologies to address the growing challenges in stroke diagnosis and pave the way for more efficient and accessible healthcare solutions.