Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model.

Autor: Zambrano-Vizuete M; Instituto Tecnológico Universitario Rumiñahui, Sangolquí, Ecuador.; Universidad Técnica del Norte, Ibarra, Ecuador., Botto-Tobar M; Eindhoven University of Technology, Eindhoven, Netherlands.; Research Group in Artificial Intelligence and Information Technology, University of Guayaquil, Guayaquil, Ecuador., Huerta-Suárez C; Instituto Tecnológico Universitario Rumiñahui, Sangolquí, Ecuador., Paredes-Parada W; Instituto Tecnológico Universitario Rumiñahui, Sangolquí, Ecuador., Patiño Pérez D; Research Group in Artificial Intelligence and Information Technology, University of Guayaquil, Guayaquil, Ecuador., Ahanger TA; Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia., Gonzalez N; Instituto de Meteorología, Criisto de La Havana, Cuba.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Aug 12; Vol. 2022, pp. 6872045. Date of Electronic Publication: 2022 Aug 12 (Print Publication: 2022).
DOI: 10.1155/2022/6872045
Abstrakt: Image segmentation and computer vision are becoming more important in computer-aided design. A computer algorithm extracts image borders, colours, and textures. It also depletes resources. Technical knowledge is required to extract information about distinctive features. There is currently no medical picture segmentation or recognition software available. The proposed model has 13 layers and uses dilated convolution and max-pooling to extract small features. Ghost model deletes the duplicated features, makes the process easier, and reduces the complexity. The Convolution Neural Network (CNN) generates a feature vector map and improves the accuracy of area or bounding box proposals. Restructuring is required for healing. As a result, convolutional neural networks segment medical images. It is possible to acquire the beginning region of a segmented medical image. The proposed model gives better results as compared to the traditional models, it gives an accuracy of 96.05, Precision 98.2, and recall 95.78. The first findings are improved by thickening and categorising the image's pixels. Morphological techniques may be used to segment medical images. Experiments demonstrate that the recommended segmentation strategy is effective. This study rethinks medical image segmentation methods.
Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper.
(Copyright © 2022 Marcelo Zambrano-Vizuete et al.)
Databáze: MEDLINE
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