Integrating convolutional and transformer networks for precise diagnosis of watershed and hemorrhagic stroke

Le résumé fourni par la source

Ischemic watershed strokes (IWS), also known as border zone infarcts hold significant importance in neurology, however, are often overlooked type of Ischemic Stroke. They are difficult to detect due to their subtle appearance and small sizes. Current diagnostic methods are limited in their ability to effectively identify IWS which represents only 5% of all Ischemic stroke cases and that is why most existing approaches for stroke segmentation and classification focus on other mostly occurring stroke types. This leaves a gap in the effective management of the disease. Additionally, machine learning models often struggle to differentiate between small and large strokes, limiting their ability to generalize across different stroke sub-types. This study aims to introduce a new method for identifying, segmenting, and classifying IWS using a fusion of CNN and Transformer models. The proposed method is evaluated using DWI MRI images. Additionally, we evaluate the proposed method on another type of stroke, hemorrhage stroke, using an open source dataset named Physio Net. The proposed CT-Transfusion is a CNN-Transformer fusion model and is embedded with a Feature Fusion Module (FFM) to integrate local and global features for enhanced stroke classification. The FFM aligns the spatial orientation of the features and unifies them at the patch level and finally, a CNN decoder reconstructs the final image. Experimental results demonstrate impressive scores with 94.79% accuracy, 93.0% precision, 95.0% recall, and 94.0% F1-Score, for the detection and classification of small ischemic watershed lesions. It outperforms benchmark models, including Modified Mobile-net-UNet, Efficient-net-Unet, DeepLabV3Plus, ResNet50, and InceptionV3. The proposed model is also assessed on haemorrhage stroke dateset characterized with relatively large size lesions. The model achieved an accuracy of 99.7%, precision of 99.8%, recall of 99.8%, and F1-Score of 99.8%. These metrics show that our proposed model outperformed the recently published models. It is concluded that the proposed dual-stream approach enhances diagnostic accuracy without needing to combine different types of medical images, making it a practical solution for stroke assessment in clinical settings. The model has multiple potential benefits: it can improve early stroke detection, assist doctors in making decisions, and potentially be integrated into automated image analysis workflows in hospitals.

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