A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

Abstract

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.