Comparative Performance Analysis of AdaBoost-Assisted Deep Learning Models for Lung Cancer Nodule Detection

Abstract

Lung cancer (LC) is among the leading causes of cancer-related deaths, and early identification of pulmonary nodules plays a crucial role in reducing mortality. This work proposes a novel hybrid framework that combines preprocessing, segmentation, feature extraction, and classification for accurate nodule detection from computed tomography scans. Digital imaging and communications in medicine images are first preprocessed using median filtering to remove noise and contrast limited adaptive histogram equalization to enhance contrast. Segmentation of lung regions is performed through histogram-based thresholding and connected component analysis. To improve feature quality, a minimum repetition and a wolf search algorithm are applied for heuristic feature selection, followed by feature learning using AdaBoost. The selected features are then classified using deep learning architectures, including LeNet, AlexNet, and VGG16, with softmax for final prediction. Experimental results show that AlexNet with SGD achieved the best performance with 97.42 % accuracy and an F1-score of 97.58 %, outperforming Adam and LeNet. Although LeNet (SGD) reached 95.9 % accuracy, its sensitivity (94.76 %) was slightly lower than that of AlexNet. The Adam optimizer provided competitive results but generally underperformed compared to SGD in both architectures. Overall, SGD-optimized AlexNet offers the most reliable balance of sensitivity, specificity, and predictive values for LC nodule detection. This integration of ensemble learning and deep neural networks enhances detection accuracy, reduces reliance on handcrafted features, and offers a robust solution to support radiologists in early LC diagnosis.