# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 1 Introduction
In some severe cases, this method may not offer effective information for professional radiologists. This is because the geometric structure overlaps during the projection process, leading to confusion regarding the actual structure. Therefore, the reconstruction of 3D scan images emerges as the preferred solution for visualizing the structural information of the patient's lesion site, facilitating clinical analysis [10]. Three-dimensional CT scan images exhibit high-resolution features. Presently, the thickness of high-resolution CT scans can range from 1 to 1.5 mm, enabling clear observation of tissue structures and lesion sites. Typically, a single scan generates dozens to hundreds of slices based on the patient's condition. However, disease diagnosis based on CT images relies on analyzing various permutations and combinations of features. This task proves exceedingly challenging even for experienced physicians. In recent years, the advancement of artificial intelligence and deep learning has significantly contributed to image classification. This technology has matured substantially, capable of extracting subtle features imperceptible to the human eye from different angles. Common examples include histogram features, texture features, shape features, and more [11]. The features of 3D CT scan images can be categorized into high-frequency and low-frequency features. High-frequency features typically represent fine structures within the image, such as edges and textures. These features correspond to rapidly changing or high-gradient areas in the image, such as object boundaries or structural textures. In CT images, they may manifest as clear contours, small tissue structures, or subtle tissue changes. On the other hand, low-frequency features usually depict the overall shape of the image, encompassing wide-ranging density changes or major tissue structures. Thesefeatures correspond to slowly changing or low-gradient regions in the image, such as the overall shape or density distribution of the tissue. In CT images, low-frequency features may appear as the general organ shape and variations in tissue density across a wide area. CT images offer diverse levels of feature information at both high and low frequencies. However, accurately discerning these features necessitates considerable time and expertise from professional doctors [12]. In a 3D CT image, multiple adjacent slices typically comprise the image. The spacing between these slices must be carefully set to ensure that the patient's disease information is not lost. In three-dimensional space, adjacent slices often overlap to some extent, ensuring the continuity of tissue structure within the CT image. This continuity entails seamless connection and smooth transition between adjacent slices, allowing for the reflection of spatial changes at the lesion site. Such continuity is crucial for doctors to make accurate diagnoses [13]. In this paper, our objective is to address the practical significance of CT image classification using the concept of multi-instance learning. We meticulously consider the fusion of high- and low-frequency features, as well as the continuity of 3D CT images, aiming to demonstrate its applicability in medical imaging through instances of 3D CT image data. Our experiments will evaluate two extensive public datasets focusing on lung cancer (TCIA) and pneumonia (CCII-CC), aiming to substantiate the superiority of our model over other methods. Figure 1 illustrates the comparison of CT slices depicting normal lungs and three types of lung cancer from the dataset utilized in this study. Many medical experts assert that scanning a greater number of CT images decreases the risk of misjudging cancer during examinations. However, CT scan images often contain extensive nodule information. Assessing a larger number of images poses a considerable challenge but is crucial for accurate diagnosis [14].