Deep Learning-Driven Approaches for Early Detection of Lung Cancer

Detecting lung cancer sooner is very much essential for improving the survival rates and reducing the mortality rate as well. This study offers an extensive approach for detecting stages of lung cancer as early as possible from CT scan images using Deep learning. Data set used in this research consists of CT scans of healthy individuals as well as of those diagnosed with various stages of lung cancer. Elastic transformation is used as preprocessing to address class imbalance. Data augmentation is applied to improve model generalization after splitting the data into training, testing and validation. Three pre-trained convolutional neural network architecture (DenseNet201, VGG16, EfficientNetB7) are employed, with transfer learning utilized to fine-tune the models for lung cancer multi-class classification. In the end, weighted CNN ensemble is implemented to combine the predictions of individual models. Popular deep learning metrics such as accuracy, recall, f1-score and precision is used to access the models performance. Final result showcases effectiveness of proposed deep learning model, with the weighted ensemble method achieving an accuracy of 98.19% on the test data. Future work includes exploring different hyper parameter tuning, incorporating additional CNN architectures, and further preprocessing techniques to enhance model accuracy.