# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 3 Proposed Methods
### 3D CT Datasets and Datasets Preprocessing  
This paper will verify the feasibility of the method on two datasets, the Cancer Imaging Archive (TCIA) and the China Consortium of Chest CT Image Investigation (CC-CCII), and the number of slices per package is controlled between 30 and 300. Table 1 presents the CT image acquisition parameter information of the two datasets. TCIA is a large public dataset on lung cancer. A total of 300 3D CT packages were used for 234 patients with lung cancer, including 216 packages of adenocarcinoma, 41 packages of small-cell carcinoma, and 43 packages of squamous-cell carcinoma. Patients needed to be fasted for at least 6 h before the examination. Whole-body emission scanning was performed within 12 min after injection of 60F-FDG (18.4 MBq/kg, 44.0 mCi/kg). During the scan, the blood glucose of each patient was less than 11 mmol/L. The original CT image was composed of anisotropic voxels with different in-plane resolutions. Due to the differences between different scanners or acquisition protocols, the voxel spacing of the CT dataset was also inconsistent. To facilitate training, we resampled all medical images according to the voxel spacing information provided by the DICOM file to ensure consistent resolution. The slice thickness range was usually between 0.625 mm and 5 mm, and the scanning mode includes plane, contrast, and 3D reconstruction. CC-CCII is a dataset of novel coronavirus pneumonia established by [PERSON] et al. [36], comprising both cases of novel coronavirus pneumonia (COVID-19) and cases of common pneumonia resulting from the SARS-CoV-2 virus. A total of 300 3D CT scans of COVID-19 and common pneumonia were included, with each scan acquired during the patient's admission examination. This dataset is essential for distinguishing between COVID-19 and other types of pneumonia, providing valuable data for developing and validating diagnostic models. Since X-ray absorption varies among different tissues or substances within the human body, Hounsfield Unit (HU) values require adjustment based on varying conditions. By referencing the absorption of water, a positive HU value signifies greater X-ray absorption by tissues or substances compared to water, while a negative HU value indicates lesser absorption than water. To delineate diverse types of abnormal tissues and lesions in the lungs, it is crucial to establish appropriate window width (WW) and window level (WL) settings to achieve clear lung features. In our experiment, we configured WW to 1050 HU and WL to \(-\)475 HU. The disparity between initial CT and modified input CT is illustrated in Figure 1. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Information** & **TCIA** & **CC-CCII** \\ \hline Slice Thickness Range & (0.625 mm, 5 mm) & (0.625 mm, 5 mm) \\ Pixel Spacing Range & (0.585937, 0.841796) & (0.594, 0.826) \\ Smallest/Largest Image Pixel Value & 0/2548 & - \\ Initial WW Range & (0. 1600) & - \\ Initial WL Range & (\(-\)700, 40) & - \\ \multicolumn{3}{c}{GE MEDICAL SYSTEMS} \\ Manufacturer & Philips & GE MEDICAL SYSTEMS \\  & SIEMENS & SYSTEMS \\  & Thorax & Siemens \\ Series Description & Recon & Toshiba \\  & ThoraxRoutine & (69, 236) \\ X-Ray Tube Current Range & (94, 450) & (69, 236) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of acquisition parameter information about TCIA and CC-CCII. Figure 2: Framework overview of the proposed HLFSRNN-MIL model. Voxel spacing denotes the distance between adjacent volume pixels in the patient's CT image within three-dimensional space. This spacing determines the minimum spatial unit discernible within the image. Smaller voxel spacing implies higher spatial resolution. Typically, the size of voxel spacing is governed by the parameter settings of the scanning device. In our experiment, the voxel spacing range of the original CT image data spanned from 0.585937 to 0.841796, while the spatial resolution range of the resampled images fell between 300 and 431.