OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN

The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cancer progression and reduce morbidity and mortality. The Faster R-CNN approach is a two-stage, high-precision nodule detection method designed for detecting small nodules, particularly in computed tomography (CT) images. This paper presents an improved Faster R-CNN by introducing an optimized multi-scale convolutional neural network (OMS-CNN) technique for feature map generation. This approach aims to achieve an optimal feature map through metaheuristic optimization by combining the last three layers of the VGG16 architecture. The advanced parameter-setting-free harmony search (PSF-HS) algorithm is utilized to implement this method, automatically adjusting the number of channels in the composite layers as a hyperparameter. The beetle antenna search (BAS) optimization algorithm is utilized to effectively initialize the kernel filter weights and biases in the composite layers, thereby enhancing training speed and detection accuracy. In the false-positive reduction stage, a combination of multiple 3D deep convolutional neural networks (3D DCNN) is designed to reduce false-positive nodules. The proposed model was evaluated using the LUNA16 and PN9 datasets. The results demonstrate that the OMS-CNN technique effectively extracted representative features of nodules at various sizes, achieving a sensitivity of 94.89% and a CPM score of 0.892. The comprehensive experiments illustrate that the proposed method can enhance detection sensitivity and manage the number of false positive nodules, thereby offering clinical utility and serving as a valuable point of reference.