Lung Cancer Detection Using Image Segmentation by means of Various Evolutionary Algorithms

Abstract

The objective of this paper is to explore an expedient image segmentation algorithm for medical images to curtail the physicians' interpretation of computer tomography (CT) scan images. Modern medical imaging modalities generate large images that are extremely grim to analyze manually. The consequences of segmentation algorithms rely on the exactitude and convergence time. At this moment, there is a compelling necessity to explore and implement new evolutionary algorithms to solve the problems associated with medical image segmentation. Lung cancer is the frequently diagnosed cancer across the world among men. Early detection of lung cancer navigates towards apposite treatment to save human lives. CT is one of the modest medical imaging methods to diagnose the lung cancer. In the present study, the performance of five optimization algorithms, namely, k-means clustering, k-median clustering, particle swarm optimization, inertia-weighted particle swarm optimization, and guaranteed convergence particle swarm optimization (GCPSO), to extract the tumor from the lung image has been implemented and analyzed. The performance of median, adaptive median, and average filters in the preprocessing stage was compared, and it was proved that the adaptive median filter is most suitable for medical CT images. Furthermore, the image contrast is enhanced by using adaptive histogram equalization. The preprocessed image with improved quality is subject to four algorithms. The practical results are verified for 20 sample images of the lung using MATLAB, and it was observed that the GCPSO has the highest accuracy of 95.89%.

Development of a machine learning-based multimode diagnosis system for lung cancer

“…It is known that radiologists distinguish the benign from malignant nodules by their size, shape, density, and other characteristics [11]. However, CT images are difficult to be analyzed manually, which requires radiologists to have excellent reading skills, especially for the diagnosis of small and isolated pulmonary nodule [12,13]. It is reported that the false positive rate of LDCT screening for lung cancer is as high as 96.4% [6].…”

“…On one hand, it is necessary to develop a method that can effectively distinguish benign from malignant CT nodules. At present, many scholars try to extract radiomic features of CT nodules and establish models to achieve the intelligent identification of benign and malignant nodules [12,14,15]. On the other hand, there is an urgent need to seek an auxiliary means, which can enhance the diagnostic efficiency of lung cancer in combination with CT. As we know, tumor markers have been widely used in the detection of lung cancer in recent years, such as progastrin-releasing peptide (ProGRP), vascular endothelial growth factor (VEGF), carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1) and neuronspecific enolase (NSE) [16,17].…”

Genetic Algorithm-Based Anisotropic Diffusion Filter and Clustering Algorithms for Thyroid Tumor Detection

“…The conventional methods in clustering analysis are the unsupervised segmentation algorithms, such as k-mean and fuzzy c-mean, where an automatic algorithm separates the given dataset (CT image) into two or more clusters. The procedure involves grouping the data of similar features into one cluster and the data points of dissimilar characteristics into another cluster [21][22].…”

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“… FC-Densenet103, Unet, DenseNet, and DenseNet121-FPN. References References [ 137 ] [ 138 ] [ 139 ] [ 140 ] [ 141 ] [ 142 ] [ 143 ] [ 129 ] [ 144 ] [ 145 ] [ 146 ] [ 147 ] [ 148 ] [ 149 ] [ 150 ] [ 151 ] [ 152 ] [ 153 ] [ 154 ] [ 155 ] [ 156 ] [ 157 ] [ 158 ] [ 159 ] [ 160 ] [ 161 ] [ 162 ] [ 163 ] [ 164 ] [ 165 ] Classification Characteristics Characteristics Gray scale feature extraction and ML classifier, and model-based techniques. Resnet-50, CNN, SVM, ResNet101, VGG16, and VGG19.…”