Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques

Abstract

Lung cancer is characterized by the uncontrollable growth of cells in the lung tissues. Early diagnosis of malignant cells in the lungs, which provide oxygen to the human body and excrete carbon dioxide because of important processes, is critical. Because of its potential importance in patient diagnosis and treatment, the use of deep learning for the identification of lymph node involvement on histopathological slides has attracted widespread attention. The existing algorithm performs considerably less in recognition accuracy, precision, sensitivity, F-Score, Specificity, etc. The proposed methodology shows enhanced performance in the metrics with six different deep learning algorithms like Convolution Neural Network (CNN), CNN Gradient Descent (CNN GD), VGG-16, VGG-19, Inception V3 and Resnet-50. The proposed algorithm is analyzed based on CT scan images and histopathological images. The result analysis shows that the detection accuracy is better when histopathological tissues are considered for analysis.