Acute ischemic stroke (AIS) is not only a common cause of disability but also a leading cause of mortality worldwide. Recent studies have shown that the collateral status could play a vital role in assessing AIS and determining the treatment options for the patients. Herein, we propose a joint regression and ordinal learning approach for AIS, built upon 3-D deep convolutional neural networks, that facilitates an automated and objective collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC-MRP). DSC-MRP images of 159 AIS subjects and 186 healthy subjects are employed to evaluate the proposed approach. The collateral status is manually assessed in arterial, capillary, early and late venous, and delay phases and served as the ground truth. The proposed method, on average, obtained 0.901 squared correlation coefficient, 0.063 mean absolute error, 0.945 Tanimoto measure, and 0.933 structural similarity index. The quantitative results between AIS and healthy subjects are comparable. Overall, the experimental results suggest that the proposed network could aid in automating the evaluation of collateral status and enhancing the quality and yield of diagnosis of AIS.
KEYWORDS: RGB color model, Endoscopy, Neural networks, Data modeling, Image classification, Detection and tracking algorithms, Performance modeling, Intestine, Image processing, Medical research
Because most of the capsule-endoscopic images contain normal mucous membranes, physicians spend most of their reading time observing normal areas. Thus, a significant reduction in their reading time would be possible if only the portion of the image frame for which a particular lesion is suspected can be read intensively. This study aims to develop a deep convolutional neural-network-based model capable of automatically detecting lesions in the capsule-endoscopic images of a small bowel. The proposed model consists of two deep neural networks in parallel, each of which takes in images in RGB and CIELab color spaces, respectively. The neural-networks model is based on transfer-learned GoogLeNet architecture. Our proposed algorithm showed promising results in classifying endoscopic images where lesions exist (98.56% accuracy). If the proposed algorithm is used to screen abnormal images, it is expected to reduce a physician's reading time and to improve his/her reading accuracy.
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