A Novel ECNN-Reconstruction of CT Scan Image using Deep Learning
Keywords:
Convolutional Neural Network, Deep Learning, Conventional Methods, Features.Abstract
Medical imaging plays a pivotal role in contemporary healthcare facilities by offering essential assistance for the accurate diagnosis and effective treatment of various disorders. The process of medical image reconstruction holds significant importance within the field of medical imaging, serving as a vital component. Its primary aim is to obtain medical images of superior quality for clinical purposes, while minimizing both the expense and potential risks to patients. Mathematical models have been extensively utilized in the field of medical image reconstruction, as well as in the broader domain of image restoration within computer vision. These models have assumed a significant position in these areas. Historically, mathematical models for image reconstruction have primarily been developed based on human knowledge and hypotheses. The primary objective of medical image reconstruction is to obtain medical images of superior quality for clinical purposes while minimizing the associated costs and risks to patients. Moreover, the process of manual restoration is characterized by a significant time investment, resulting in a substantial accumulation of tasks. This study investigates the utilization of deep learning methodologies to enhance the efficacy of picture reconstruction from fuzzy photos, while concurrently mitigating computing demands. Image reconstruction is achieved by the utilization of an Enhanced Convolutional Neural Network (ECNN), which operates on the principles of Deep Learning. The Fuzzy Granular Filter is employed at the preprocessing stage for the purpose of noise elimination, while the feature map is utilized to enhance the features. The collected features are subsequently fed into the convolutional layers, resulting in an enhancement of quality at each layer. The accuracy of the proposed approach is enhanced in comparison to traditional methods.
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