DKCDF: Dual-Kernel CNN with Dual Feature Fusion for Lung Cancer Detection

Wariyo Arero, Yaqin Zhao, Longwen Wu, Yi Wang

2024

Abstract

One of the main reasons for cancer-related fatalities worldwide is lung cancer. Early diagnosis is essential for enhancing patient outcomes and lowering mortality rates. Deep learning-based approaches have recently demonstrated promising outcomes in medical image analysis applications, such as lung cancer identification. In order to improve lung cancer detection, this research suggests a unique method that combines a dual-kernel convolutional neural network (DKC) with dual-feature fusion using the Histogram of oriented gradients (HOG) and local binary patterns (LBP). Convolutional neural networks are good at extracting and detecting features. CNN features are built using low-level features from the first convolution layer, which might only partially capture some local features and lead to the loss of some crucial details like edges and contours. HOG is quite good at describing the shape of objects. LBP can record local structure and information about spatial texture. The distribution of edge directions or local gradients in intensity can provide a good definition of an object’s shape and local appearance. The lung image is loaded with bone, air, blood, water and other substances and appears noisy in the lung image. As a result, in this research, we favor the HOG and LBP feature fusion for lung cancer detection.

Paper Citation

in Harvard Style

Arero W., Zhao Y., Wu L. and Wang Y. (2024). DKCDF: Dual-Kernel CNN with Dual Feature Fusion for Lung Cancer Detection. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES; ISBN 978-989-758-688-0, SciTePress, pages 54-64. DOI: 10.5220/0012406100003657

in Bibtex Style

@conference{biodevices24,
author={Wariyo Arero and Yaqin Zhao and Longwen Wu and Yi Wang},
title={DKCDF: Dual-Kernel CNN with Dual Feature Fusion for Lung Cancer Detection},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES},
year={2024},
pages={54-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012406100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES
TI - DKCDF: Dual-Kernel CNN with Dual Feature Fusion for Lung Cancer Detection
SN - 978-989-758-688-0
AU - Arero W.
AU - Zhao Y.
AU - Wu L.
AU - Wang Y.
PY - 2024
SP - 54
EP - 64
DO - 10.5220/0012406100003657
PB - SciTePress