Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model

Marcelo Zambrano-Vizuete, Miguel Botto-Tobar, Carmen Huerta-Suárez, Wladimir Paredes-Parada, Darwin Patiño Pérez, Tariq Ahamed Ahanger, Neilys Gonzalez

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

6 Citaten (Scopus)
41 Downloads (Pure)

Samenvatting

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.

Originele taal-2Engels
Artikelnummer6872045
Aantal pagina's9
TijdschriftComputational Intelligence and Neuroscience
Volume2022
DOI's
StatusGepubliceerd - 2022

Bibliografische nota

Publisher Copyright:
© 2022 Marcelo Zambrano-Vizuete et al.

Vingerafdruk

Duik in de onderzoeksthema's van 'Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model'. Samen vormen ze een unieke vingerafdruk.

Citeer dit