Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification

TL;DR

This paper presents an advanced deep learning approach using DenseNet201, Focal Loss, and data augmentation to improve lung cancer detection accuracy from CT images, addressing dataset imbalance and overfitting issues.

Contribution

Introduces a novel lung cancer detection method combining DenseNet201 with techniques to handle data imbalance and overfitting, achieving high accuracy.

Findings

Achieved 98.95% accuracy in lung cancer detection.

Effectively addressed dataset imbalance with Focal Loss and data augmentation.

Demonstrated the effectiveness of the proposed approach over existing methods.

Abstract

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.