Detection of Stroke in CT Scans Using Deep Learning and Image Processing Techniques

This work describes a robust paradigm for inferring strokes from CT scans using deep reinforcement learning and image analysis. The steps which are as follows: first, a large volume of high quality CT scan images will be gathered second, the pre-processing of the scan images to improve the image quality and third, an advanced CNN model will be designed for accurate stroke detection. This performance has been further enhanced by integrating data augmentation and the application of data transfer learning, where the model reaches an accuracy of 94. AUC-ROC of 0. 2% for the mice group and an AUC-ROC of 0. 2% for the rats group. 97. In conclusion, the structural and functional understanding of the model proves that the model is sensitive coupled with high specificity and was able to generalize beyond the data used in developing this model and across the different types of strokes including ischemic, hemorrhagic, and transient ischemic strokes. Comparing with benchmark models, it is evident that the proposed approach outperforms the baseline counterparts considerably. These outcomes can be concluded as the fact that computer-aided methods can help radiologists in the quick and accurate decision making with a possibility of positive changes to the disease progression. The future research areas on the proposed method are the increase in model interpretability and the incorporation of other modalities in an effort to increase the accuracy of the results. The study thus establishes the potential of Deep Learning in overhauling the conventional medical diagnosis and especially in timely accurate identification and diagnosis of strokes thus enriching health care.