A HYBRID FEATURE FUSED DEEP LEARNING APPROACH FOR LUNG CANCER CLASSIFICATION
DOI:
https://doi.org/10.48047/zddezk10Keywords:
deep learning, Google net, classification, customized CNN, feature extraction, semantic and intensity.Abstract
In today’s world medical images plays a crucial role in treating lung cancer. Lung cancer
is the riskiest cancer. It is difficult to cure in the advanced stages. The survival rate can be increased
only by effective early detection. The previous work mainly focuses only on the semantic features,
which does not hold the texture and edge information of images. In order to overcome this problem.
Firstly, this article explores how to identify the relation between feature extraction and
classification approach using both semantic and intensity information for classifying lung cancer
images as Benign, malignant and normal. Secondly, the selected optimal features reduced using
correlated intensity property-based techniques.
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