Automated lung nodule detection in CT images by optimized CNN: impact of improved whale optimization algorithm

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 itsmalicious nature. Early detection of the disease is very significant for patients’ survival, andit is a challenging issue. Therefore, a new model including the following stages: (1) imagepre-processing, (2) segmentation, (3) proposed feature extraction and (4) classificationis proposed. Initially, pre-processing takes place, where the input image undergoes specificpre-processing. The pre-processed images are then subjected to segmentation, which iscarried out using the Otsu thresholding model. The third phase is feature extraction, wherethe 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 moreprecise detection of a lung nodule, the filter size of a convolution layer, hidden unit inthe fully connected layer and the activation function in CNN are tuned optimally byan improved whale optimization algorithm (WOA) called the whale with tri-level enhancedencircling behavior (WTEEB) model.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).