Summary
In recent decades, intracranial hemorrhage detection from computed tomography (CT) scans has gained considerable attention among researchers in the medical community. The major problem in dealing with the Radiological Society of North America (RSNA) dataset is a three dimensional representation of CT scan, where the labeled data are scarce and hard to obtain. To highlight this problem, a novel learned fully connected separable convolutional network is proposed in this research article. After collecting the CT scans, data augmentation is used to generate multiple image variations to improve the capacity of the proposed model generalization. Based on the albumentations library, the transformations are selected for data augmentation such as brightness adjustment, horizontal flipping, shifting, rotation, and scaling. The intracranial hemorrhage subtype classification is accomplished utilizing a learned fully connected separable convolutional network which significantly classifies six classes as any, intraparenchymal, subarachnoid, epidural, intraventricular, and subdural. In the resulting phase, the learned fully connected separable convolutional network obtained an average accuracy of 98.63%, sensitivity of 73.32%, specificity of 99.49%, and area under the curve of 98.98%, where the obtained results are effective compared with ResNet‐50, SE‐ResNeXt‐50, ResNeXt‐101, and ResNeXt‐101 with bidirectional long short term memory network.
Developing reusable components are one of the main objectives of component-based software engineering. They play a crucial role in the field of application development and support. CBSE use certain architectural patterns and infrastructures of standard software to increase overall product quality. CBSE apply two parallel engineering activities, domain engineering and component-based development (CBD). Domain analysis explores the application domain with the intent of finding functional, behavioural, and data components that are candidates for reuse and places them in the reuse repository. Strategies for developing adaptive reusable components using top-down domain analysis leads to good quality in the component. Domain analysis promotes strategies and models that have been developed for their specific areas. Therefore, these models are suitable for their own domain, but may not be entirely suitable for domain analysis of other domains. So, developing the reusable components using the top down domain analyses existing components. This paper describes how to build a domain to use top-down analysis of reusable software components.
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