Automated Lung Nodule Detection in CT Images by Optimized CNN: Impact of Improved Whale Optimization Algorithm

M. Kiran Kumar; Anthoniraj Amalanathan

doi:10.24423/cames.372

M. Kiran Kumar Vellore Institute of Technology
Anthoniraj Amalanathan Vellore Institute of Technology

DOI: http://dx.doi.org/10.24423/cames.372

Abstract

Lung cancer is one of the leading causes of cancer-related deaths among individuals. It should be diagnosed at the early stages, otherwise it may lead to fatality due to its malicious nature. Early detection of the disease is very significant for patients’ survival, and it is a challenging issue. Therefore, a new model including the following stages: (1) image pre-processing, (2) segmentation, (3) proposed feature extraction and (4) classification is proposed. Initially, pre-processing takes place, where the input image undergoes specific pre-processing. The pre-processed images are then subjected to segmentation, which is carried out using the Otsu thresholding model. The third phase is feature extraction, where the major contribution is obtained. Specifically, 4D global local binary pattern (LBP) features are extracted. After their extracting, the features are subjected to classification, where the optimized convolutional neural network (CNN) model is exploited. For a more precise detection of a lung nodule, the filter size of a convolution layer, hidden unit in the fully connected layer and the activation function in CNN are tuned optimally by an improved whale optimization algorithm (WOA) called the whale with tri-level enhanced encircling behavior (WTEEB) model.

Keywords

lung disease, pre-processing, segmentation, feature extraction, classification, performance,

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