Hybrid deep convolution model for lung cancer detection with transfer learning
This work addresses the challenge of improving diagnostic precision for lung cancer, a leading cause of cancer mortality, though it appears incremental as it builds on existing deep learning approaches.
The paper tackled the problem of early and accurate lung cancer detection by introducing a hybrid deep convolution model with transfer learning, achieving 98% accuracy and 97% sensitivity in distinguishing lung cancer from CT scans.
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.