Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm

Abstract

A quarter of all cancer deaths are due to lung cancer. Studies show that early diagnosis and treatment of this disease are the most effective way to increase patient life expectancy. In this paper, automatic and optimized computer-aided detection is proposed for lung cancer. The method first applies a preprocessing step for normalizing and denoising the input images. Afterward, Kapur entropy maximization is performed along with mathematical morphology to lung area segmentation. Afterward, 19 GLCM features are extracted from the segmented images for the final evaluations. The higher priority images are then selected for decreasing the system complexity. The feature selection is based on a new optimization design, called Improved Thermal Exchange Optimization (ITEO), which is designed to improve the accuracy and convergence abilities. The images are finally classified into healthy or cancerous cases based on an optimized artificial neural network by ITEO. Simulation is compared with some well-known approaches and the results showed the superiority of the suggested method. The results showed that the proposed method with 92.27% accuracy provides the highest value among the compared methods.