Lung Cancer Detection and Classification from Chest CT Images Using an Ensemble Deep Learning Approach

Nowadays, Cancer's devastating impact is growing, taking thousands of lives prematurely each day. Lung cancer stands at the forefront of this grim reality. Timely and accurate cancer diagnosis is crucial, as it directly correlates with effective treatment and improved patient outcomes. In this paper, we proposed an ensemble deep-learning method for detecting and classifying lung cancers that greatly impact the Computer Aided Diagnosis (CAD) system. Initially, three deep convolutional neural networks (CNN) Transfer Learning Approaches, MobileNetV2, VGG19, and Resnet50, were used individually to perform classification. Then, these models are combined to perform better in lung cancer diagnosis using the fusion of chest CT and PET-CT images. This approach leverages the strengths of MobileNetV2, VGG19, and ResNet50's pretrained weights for feature extraction, and then the extracted features are concatenated and used for classification through the weighted average ensemble technique. After an extensive experimental analysis, the proposed ensemble model achieved a test accuracy of 98.93%, which is better than the individual model performance (98.67% in MobileNetV2, 98.20% in VGG19, and 97.67% in ResNet50). It can be an efficient diagnostic tool for lung cancer detection, as the prediction results of the proposed deep learning model outperform the recent Transfer Learning approaches.