March 3, 2026

Hybrid deep learning approach for improving the diagnosis of lung cancer disease

Key Points

The proposed model achieved an accuracy of 99.30% and an AUC of 96.6% for dataset 2, indicating a significant improvement in detection.
Enhanced MobileNetV3 predicts lung cancer probability effectively, utilizing deep learning techniques for better accuracy.
The DenseNet-169 model is employed for feature extraction, enhancing the overall classification performance of the system.
Optimization via an osprey algorithm ensures the classification parameters are finely tuned for even higher accuracy.

Abstract

Lung cancer is recognised as the most severe disease that affects humans and frequently results in mortality when compared to other cancer conditions. Lung cancer cannot be detected early because it exhibits no symptoms. However, early identification of lung cancer contributes to people's continued survival rate. Computer technology has recently been employed to solve these diagnostic issues. In this research, we propose a hybrid deep-learning method for predicting lung cancer. An enhanced MobileNetV3 (EMobileNetV3) is proposed to predict the probability of lung cancer. The DenseNet-169 model is used to extract the features. An effective osprey optimisation algorithm (Os-OA) is also presented to adjust the proposed classification model's parameters to improve the classification performance. Compared to other existing models, the proposed model performed better, obtaining an accuracy of 98% and an AUC of 96% for dataset 1 and an accuracy of 99.30% and an AUC of 96.6% for dataset 2.

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