Authors:
Wariyo G. Arero
1
;
Yaqin Zhao
1
;
Longwen Wu
1
and
Yi Wang
2
Affiliations:
1
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
;
2
Fushan Environmental Monitoring Center, Yantai, China
Keyword(s):
Multi Feature Fusion, CNN, Lung Cancer, HOG, LBP.
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.
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