Lung cancer detection in CT scans using extracted nodule features and precise multistage segmentation with deep learning based ensemble model.

Lung cancer is one of the deadliest malignancies, known for rapid growth and has high potential of spreading the original tumor cells to other cells of the body. According to GLOBOCAN 2020, there were 2.2 million new lung cancer cases and 1.8 million deaths, making up 18% of global cancer deaths. The American Lung Association (ALA) reports that only 25.8% of cases are detected early, with a 5-year survival rate. Early detection is crucial but remains difficult due to nonspecific symptoms and current imaging limitations. This study proposes a deep Convolutional Neural Network (CNN) approach to improve lung cancer detection by facilitating accurate, automated diagnosis of lung nodules in early stage cancer. We combine two advanced deep learning models, VGG16 and U-Net++, to enhance the classification of medical images. The approach leverages VGG16's strengths in feature extraction and U-Net++'s robust multiscale processing to handle the diverse shapes and sizes of tumors in lung tissue. These models are ensembled to advance our capabilities in processing medical images. Features extracted from both models are concatenated to create a comprehensive and flexible representation of the input data. The feature set is flattened and then undergoes further processing using Dense layers, Batch Normalization, and Dropout layers to improve generalization and prevent overfitting. The final output layer classifies the images as 'Benign,' 'Malignant,' or 'Normal.' Extensive experiments on a lung image dataset demonstrate significant classification improvements over individual models. Performance evaluation using metrics such as accuracy, precision, recall, and F1-score shows higher results for the ensemble model. This study concludes that integrating VGG16 and U-Net + + into this CNN architecture can significantly enhance lung cancer detection performance by achieving a remarkable accuracy of 96% and provide a reliable tool for clinicians in early stage diagnosis and treatment monitoring. Our proposed brings improvement in accuracy, segmentation and feature extraction and thereby proves to be one among the top models available.