Deep Neural Network Approach for Lung Cancer Detection in CT Images

Abstract

Early detection of lung cancer is the key to the better survival of the patient but the problem is that many cases cannot be identified until they reach advanced stages because of the weakness of the manual image analysis in spite of the fact that ct scanning offers a detailed image of lung structures the interpretation of these images cannot be done successfully without a lot of clinical experience and due caution in an effort to assist the medical staff this paper aims to develop an intelligent system of deep learning to predict lung cancer through ct scan images the framework that has been built is based on the convolutional neural network which automatically identifies the important visual patterns which correlate with abnormal lung tissues the images obtained are upgraded by refinement before training through noise elimination scaling of images normalization of sizes and artificial image creation to enhance reliability of learning the trained model does binary classification to differentiate between the healthy cases and the potentially cancerous cases to ensure reliable validation accuracy precision recall and f1-score are among several statistical measurements of system performance the results of the experiment suggest that the suggested method provides stable and consistent predictions on various sets of data the system minimizes diagnostic variability and diagnostic dependency it allows quick and objective analysis eliminating the need to rely on manual inspection the proposed solution will serve as a supportive clinical aid allowing an earlier diagnosis helping to plan the treatment and enhancing the efficiency of healthcare in the management of lung cancer in general