Stroke lesion detection using convolutional neural networks
Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results.
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Datasets
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Stroke Classification | CT Lesion Stroke Dataset | PSO+CNN (Cifar-10, 50/50, Original) | Average Class Accuracy | 93.46 | # 2 | |
| Stroke Classification | CT Lesion Stroke Dataset | PSO+CNN (Cifar-10, 75/25, Cranium Segmented) | Average Class Accuracy | 98.86 | # 1 |
