Hybrid model detection and classification of lung cancer

Abstract

Lung cancer ranks among the most prevalent malignancies worldwide. Early detection is pivotal to improving treatment outcomes for various cancer types. The integration of artificial intelligence (AI) into image processing, coupled with the availability of comprehensive historical lung cancer datasets, provides the chance to create a classification model based on deep learning, thus improving the precision and effectiveness of detecting lung cancer. This not only aids laboratory teams but also contributes to reducing the time to diagnosis and associated costs. Consequently, early detection serves to conserve resources and, more significantly, human lives. This study proposes convolutional neural network (CNN) models and transfer learning-based architectures, including ResNet50, VGG19, DenseNet169, and InceptionV3, for lung cancer classification. An ensemble approach is used to enhance overall cancer detection performance. The proposed ensemble model, composed of five effective models, achieves an F1-score of 97.77% and an accuracy rate of 97.5% on the IQ-OTH/NCCD test dataset. These findings highlight the effectiveness and dependability of our novel model in automating the classification of lung cancer, outperforming prior research efforts, streamlining diagnosis processes, and ultimately contributing to the preservation of patients' lives.