Hybrid deep convolution model for lung cancer detection with transfer learning

TL;DR

This paper presents a hybrid deep learning model called MSNN that leverages transfer learning to improve the accuracy and sensitivity of lung cancer detection from CT scans, achieving 98% accuracy and 97% sensitivity.

Contribution

The paper introduces the Maximum Sensitivity Neural Network (MSNN), a novel hybrid deep convolution model that enhances lung cancer detection accuracy using transfer learning and sensitivity mapping.

Findings

Achieved 98% overall accuracy in lung cancer detection.

Attained 97% sensitivity, reducing false negatives.

Enabled visualization of malignant regions via sensitivity maps.

Abstract

Advances in healthcare research have significantly enhanced our understanding of disease mechanisms, diagnostic precision, and therapeutic options. Yet, lung cancer remains one of the leading causes of cancer-related mortality worldwide due to challenges in early and accurate diagnosis. While current lung cancer detection models show promise, there is considerable potential for further improving the accuracy for timely intervention. To address this challenge, we introduce a hybrid deep convolution model leveraging transfer learning, named the Maximum Sensitivity Neural Network (MSNN). MSNN is designed to improve the precision of lung cancer detection by refining sensitivity and specificity. This model has surpassed existing deep learning approaches through experimental validation, achieving an accuracy of 98% and a sensitivity of 97%. By overlaying sensitivity maps onto lung Computed Tomography (CT) scans, it enables the visualization of regions most indicative of malignant or benign classifications. This innovative method demonstrates exceptional performance in distinguishing lung cancer with minimal false positives, thereby enhancing the accuracy of medical diagnoses.