Lung Cancer Prediction Using Deep Learning Framework

Abstract

Lung carcinoma also known as lung cancer is one of the dangerous diseases caused all over the world. It is caused due to the reluctant increase of cells in the lung tissues. It is cured only during its early stage, by starting the treatment. This is detected using the Computed Tomography (CT) scanning and blood test reports. By blood test, the tumour is detected after the humans affected with a minimum span of 4 years. So, to know the early stage of cancer, CT scanning is used. The CT images are classified into normal and abnormal. The abnormal image is detected by focusing on the tumour portion. The dataset in jpg format, composed of Computed Tomography (CT) images. The proposed model is trained by using the Convolutional Neural Network (CNN). Pretrained ImageNet models including LeNet, AlexNet and VGG-16, are used to detect lung cancer. The proposed model uses AlexNet model and the features obtained from the last fully connected layer of the model were separately applied as input to the softmax classifier. The combination of AlexNet and softmax layer together has given an accuracy of 99.52%. The proposed model serves as a consistent and sustainable diagnosis model for lung cancer detection.