Hybrid deep convolution model for lung cancer detection with transfer learning
Journal:
arXiv
Published Date:
Jan 6, 2025
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.
