Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 9
• Main Authors: Zambrano-Vizuete, Marcelo, Botto-Tobar, Miguel, Huerta-Suárez, Carmen, Paredes-Parada, Wladimir, Patiño Pérez, Darwin, Ahanger, Tariq Ahamed, Gonzalez, Neilys
• Format: Journal Article
• Language: English
• Published: United States Hindawi 12.08.2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Artificial neural networks; Brain; CAD; CAD-CAM systems; Computer aided design; Computer programs; Computer vision; Datasets; Deep Learning; Feature extraction; Ghosts; Image acquisition; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Information processing; Machine learning; Machine vision; Magnetic resonance imaging; Medical imaging; Neural networks; Neural Networks, Computer; Radiation; Semantics; Sensors; Software; Thickening
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/6872045

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.