Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification
This work addresses the critical issue of false positives in lung cancer diagnosis for medical applications, but it is incremental as it builds on existing deep learning models with standard enhancements.
The paper tackled the problem of low accuracy in lung cancer detection from CT images due to small and imbalanced datasets, achieving a result of 98.95% accuracy using an approach based on DenseNet201 with techniques like Focal Loss and data augmentation.
Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing a small and imbalanced dataset. This paper introduces an innovative approach for LC detection and classification from CT images based on the DenseNet201 model. Our approach comprises several advanced methods such as Focal Loss, data augmentation, and regularization to overcome the imbalanced data issue and overfitting challenge. The findings show the appropriateness of the proposal, attaining a promising performance of 98.95% accuracy.