Lung Cancer Prediction from CT Images and using Deep Learning Techniques
Botla Mamatha, D Rashmi, Kanchan S. Tiwari, Poornima A Sikrant, A. Arokiaraj Jovith, Pundru Chandra Shaker Reddy
Abstract
Cellular proliferation in the lung tissues is a hallmark of lung cancer. Lung cancer is particularly dangerous since the lungs are responsible for both breathing in oxygen and exhaling carbon dioxide—two of the body's most vital functions. The application of deep-learning (DL) for the identification of lymph node involvement on histopathology slides has gained a lot of attention due to the potential impact it could have on patient diagnosis and therapy. Recognition accuracy, precision, sensitivity, F-Score, specificity, etc., are all significantly lower with the current approach. Convolution-Neural-Network (CNN), CNN Gradient-Descent (CNN GD), VGG-16, VGG-19, and Resnet-50 are just few of the deep learning algorithms that exhibit improved performance in the metrics with the proposed methodology. CT scan pictures and histopathology images are used to evaluate the suggested method. When histopathological tissues are analyzed, the results demonstrate that detection accuracy improves.