Toward Hybrid Deep Convolutional Neural Network Architectures For Medical Image Processing

Loukil, Zainab and Al-Majeed, Salah (2021) Toward Hybrid Deep Convolutional Neural Network Architectures For Medical Image Processing. In: International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), 2020.

Full content URL: https://doi.org/10.1109/3ICT51146.2020.9312027

Full text not available from this repository.

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Artificial Intelligence (AI) has gained a great interest in improving systems and mechanisms. The talent of AI has been shown through its upgraded technologies including Deep Learning (DL), which has proven valuable performances in image processing. The enhancement of image treatment and analysis, particularly medical imaging, has became one of the most important steps toward the improvement of several systems in different applications such as medical treatment, analysis, and prognosis systems. Deep Convolutional Neural Network (CNN) presents one of the most applied DL approach in medical imaging. CNN covers more than one architecture which historically proved relevant performance results. In this paper, An analytical review of selective CNN architectures including ResNet, DenseNet, and wider networks, particularly Inception-V4, will be presented. Toward the need for a hybrid algorithm, a proposed architecture composed of two different CNNs will be defined by focusing mainly on the conveniences of both DenseNet and Inception-V4 networks.

Divisions:College of Science > School of Computer Science
ID Code:46730
Deposited On:28 Sep 2021 15:33

Repository Staff Only: item control page