An Approach for Lung Cancer Detection using SMOTE with Convolutional Network

Abstract

With a significant rise in lung cancer cases globally, especially among both men and women, effective lung cancer detection techniques are critically important. This paper addresses the urgency by employing deep learning techniques for lung cancer detection. Our study is based on categorizing the images into three distinct classes: benign, malignant, and normal cases. To ensure uniformity, images of varying sizes are standardized. Addressing the challenge of data imbalance, this paper employs the “Synthetic Minority Over-sampling Technique” (SMOTE), and further enhance image quality through Gaussian Blur in the preprocessing phase. Subsequently, a “Convolutional Neural Network” (CNN) model named “ImageTriNet”, compare its performance with transfer learning models. The ImageTriNet model exhibits commendable results, after 13 training epochs, attaining an accuracy of 0.98, precision of 0.99, recall of 0.96, and an F1-score of 0.97. This research contributes to the ongoing efforts in leveraging deep learning techniques for accurate and timely detection of lung cancer, showcasing efficacy of our ImageTriNet model in this critical domain.