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Tuberculosis (TB) is an infectious disease caused by mycobacterium tuberculosis that is one of the leading causes of human health issues and death (Elsayed et al., 2021). As defined in the imaging classification of the PTB industry diagnostic standard of the People's Republic of China “WS 288-2017”, PTB is the most common type of tuberculosis and occurs mostly in adults. The lesions tend to be mostly located in the superior lobe apicoposterior segment and lower lobe dorsal segment. Etiological examination first requires qualified test specimens, and sputum smear sensitivity is low (Steingart et al., 2006; Zheng, R. et al., 2022). Mycobacterium culture is the reference standard (Yan et al., 2017), but it takes a long time, approximately eight weeks, and requires effective laboratory quality control of specimen contamination rates. Histopathological examination is an invasive examination that involves obtaining tissue samples through percutaneous lung puncture or bronchoscopic biopsy for pathological diagnosis (Badr et al., 2022).
Chest CT imaging provides an additional diagnostic modality that is often used in clinical practice. The lesions of PTB are very complex and diverse (Iliyasu et al., 2018; Wang et al., 2022), and CT signs of active PTB include cavitation, pulmonary nodules, tree-in-bud signs, and consolidation (patchy proliferative lesions) (Wetscherek et al., 2022). These signs have clinical diagnostic value. Rapid and accurate diagnosis of TB remains a challenge for clinicians. However, all these diagnostic tests were evaluated by professional radiologists who must expend considerable time and effort to make accurate diagnostic decisions in daily work. This approach may not be suitable for real-time screening, and there is high variability between and within observers (Nel et al., 2022; Owais et al., 2020).
There are many methods for classifying diseases and segmenting images. Some scholars have studied the multi-feature fusion model of image classification using denoising convolutional neural networks and attention mechanisms (Zhang et al., 2023). The classification of breast cancer was based on the improved whale optimization algorithm and compared with other methods (Devi et al., 2023). Based on the interactive medical image segmentation framework of optimized swarm intelligence and convolutional neural networks, a method combining convolutional neural networks and swarm intelligence was proposed to optimally identify the required regions (Kaushal et al., 2022). However, a segmentation framework based on swarm intelligence and the grasshopper optimization algorithm (GOA) was used to successfully carry out feature extraction. The defect is that only three lesion images were trained (Thapar et al., 2022).
Object detection is the foundation of artificial intelligence. It was first proposed by Wax in 1955 (Wax et al., 1955). In recent years, deep learning has also applied some algorithms to object detection. YOLO (You Only Look Once) is an object recognition and location algorithm based on a deep learning neural network (Shelatkar et al., 2022). The most important features of the system are its fast running speed and small size, which can be used for real-time system monitoring (Baccouche et al., 2022; Luo et al., 2021). Its core idea was to use the whole image as the input terminal, and features were extracted through the network. The local features of CT images were analyzed by the classifier, and the position and category of the detected target regression boundary box were output through the output layer. YOLOv5 is a single-stage object recognition algorithm (Huang et al., 2022). It has adopted the best optimization strategy in the field of convolutional neural networks (CNNs) in recent years. YOLOv5 can run on most normal computers in hospitals. The greatest advantage was that the calculation results were very good.