Multi-label lung disorder diagnosis via hybrid handcrafted and Swin transformer-based global–local feature fusion

Background Cancer is a major health issue caused by various chemical imbalances and genetic problems, ranking as the second leading cause of death worldwide. Approximately one in six individuals succumb to this disease. Lung cancer is among the most prevalent and lethal types, significantly affecting human health due to its high mortality rate. Objectives Early detection is vital in combating this life-threatening illness, as it greatly enhances survival rates. Computed tomography (CT) scans are key diagnostic tools for lung cancer, but their high cost can limit access and lead to varied interpretations by different observers. Moreover, analyzing these images demands significant time and expertise. Recent advancements in deep learning (DL) have accelerated lung cancer detection. Methods This paper presents a flexible and scalable feature-fusion and classification pipeline to enhance computer vision tasks by integrating features from multiple sources and utilizing deep sequential modeling. The framework combines high-level representations from the Shifted Window (Swin) transformer and handcrafted descriptors from the Histogram of Oriented Gradients (HOG), enabling a thorough fusion of semantic, structural, and texture-based information. A support vector machine (SVM) classifies by learning sequential relationships among the fused features. The proposed hybrid Swin-HOG-SVM model allows pathologists to effectively and affordably evaluate more patients, leading to improved healthcare outcomes. This optimized model assists in the early detection of lung cancer, reducing the necessary effort, time, and costs. Results Experimental results demonstrate that the proposed Swin–HOG–SVM pipeline outperforms standalone deep and handcrafted approaches across multiple evaluation metrics, achieving improved accuracy, precision, recall, and F1-score, achieving an accuracy of 99.39 %, specificity of 97.78 %, precision of 99.42 %, recall of 97.78 %, and an F1-score of 98.56 %. Conclusions This innovative classifier shows significant promise in accurately and efficiently detecting lung tumors, providing a valuable tool for oncology specialists looking to streamline the diagnostic process.