FocalNeXt: A ConvNeXt augmented FocalNet architecture for lung cancer classification from CT-scan images

Abstract

Early and accurate diagnosis of lung cancer, a life-threatening disease, is critical to the successful treatment of patients with the disease. On the other hand, it is well known that the integration of computer-aided diagnosis (CAD) systems into the diagnostic workflow facilitates the process and improves the diagnostic results of automated systems by reducing the difficulties associated with human observation. In this paper, we present FocalNeXt, a new ConvNeXt-augmented FocalNet architecture specifically designed for automated lung cancer detection from computed tomography (CT) scan images. By combining the strong attention mechanism of FocalNet and the feature extraction mechanism of ConvNeXt within the vision transformer paradigm, FocalNeXt aims to improve diagnostic accuracy. Our comprehensive evaluation of a publicly available Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) CT-scan dataset includes rigorous comparisons and an ablation study. FocalNeXt achieved an accuracy of 99.81%, outperforming the state-of-the-art methods. The model also excelled in sensitivity (99.78%), recall (99.36%), and F1-score (99.56%), positioning FocalNeXt as a leading model for lung cancer detection. The ablation study further demonstrated its efficacy and underscored the robustness of FocalNeXt in different configurations. The results underline its potential to contribute to advances in medical imaging and personalized healthcare.