An Explainable Ensemble Convolutional Neural Network for Early Lung Cancer Prediction with Web Application

Abstract

Lung Cancer is one of the deadliest forms of cancer significantly contributing to the rising mortality rates globally. The high mortality rate associated with lung cancer can largely be attributed to its late detection, as symptoms often do not appear until the disease has reached advanced stages. Early detection of anomalies in medical imaging, particularly at the initial stages, is crucial for advancing both quantitative image analysis and patient care. In this context, our research introduces a fully automated web application to predict lung cancer early on CT scan images. This application leverages the power of a weighted average ensemble-based deep learning framework that combines multiple neural network architectures to enhance the reliability of automated classification. The work highlights the need for interpretability in clinical decision support systems, going beyond classification. Our proposed approach is organized into three independent phases. First, we performed image augmentation as part of the preprocessing stage analyzing the IQ-OTH lung cancer dataset which implies that the model is trained on diverse and enriched input data. The system incorporates ResNet50, VGG16, and a custom CNN, all of which provide complementary feature-learning capabilities that improve overall predictive dependability. The ensemble model clearly outperformed the individual networks, achieving an accuracy of 92.28% on the IQ-OTH lung cancer dataset. The visual indications and regions that most influence our model’s decisions are highlighted using Explainable Artificial Intelligence techniques, particularly LIME and SHAP. By making the model’s inner workings more understandable, these explanations hope to boost medical practitioners’ confidence.

Finally, the ensemble model was integrated into a user-friendly web application allowing users to upload CT scan images, which are then analyzed to classify the images into normal, benign, or malignant categories. Furthermore, our web application gives confidence levels for each prediction increasing the credibility of its results. Our system provides medical practitioners with a smooth and effective tool by integrating the precision of an ensemble model with the flexibility of a web application.