Lung Cancer Detection Using Deep Learning-Based Convolutional Neural Networks

Lung cancer is the leading cause of death in individuals, as its symptoms only become evident in its advanced stages, making it difficult to diagnose. It has a high mortality rate compared to other types of cancer. Therefore, the early detection of lung cancer is crucial for evaluating patients and improving their chances of responding positively to treatment, which is the most challenging way to increase their survival rates. In this study, a computer-assisted classification method for diagnosing lung cancer has been used. This method utilizes an evolutionary approach that integrates architectural evolution with weight learning through neural networks. There are various techniques proposed for lung cancer prognosis. However, in this work, a convolutional neural network model has been developed which improves the performance and provides a more accurate evaluation of whether lung cancer is malignant or not. The categorization process was performed, and the results were assessed using multiple performance metrics including an accuracy of 94%. Furthermore, this method allows medical practitioners to early diagnose the presence of lung cancer which may lead to better health treatment of the patients.