The Deep Hybrid Neural Network and an Application on Polyp Detection

Document Type

Article

Publication Date

3-30-2024

Abstract

Mathematical morphology and convolution operators are two different methods to extract the characteristics and structures of images. Over the past decades, Deep Convolutional Neural Networks (DCNN) have been proven to be more powerful than traditional image-processing approaches. In this paper, we propose a novel structure called Deep Hybrid Neural Network (DHNN) by taking advantage of the convolution and morphological neural layers. Its practical application to polyp detection in medical images is illustrated. For experimental completeness, we adopt nine polyp image datasets, including publicly available data and our own collected data. For performance comparisons, we select three backbone models. Experimental results show that our DHNN achieves the best performance in comparisons in terms of computational complexity and accurate performance.

Identifier

85190865461 (Scopus)

Publication Title

International Journal of Pattern Recognition and Artificial Intelligence

External Full Text Location

https://doi.org/10.1142/S0218001424520098

e-ISSN

17936381

ISSN

02180014

Issue

4

Volume

38

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