## 3 Methods
### Data Preprocessing  
Before extracting meaningful features, raw data must undergo preprocessing to isolate the ROI for optimal model performance. Many studies segment the ROI from unprocessed images using either annotation coordinates or basic morphological techniques. However, medical images often contain significant disruptions, such as small blood vessels, bronchi, and peripheral distortions, which can severely affect model accuracy. To address this, our approach employs a series of processing steps to remove image noise, followed by precise segmentation and isolation of the ROI. The procedural flow is illustrated in Figure 2.  
Figure 1: ReAcc_MF algorithm flowchart.  
Figure 2: Data preprocessing process. The symbol ‘\(\times\)’ represents the element-wise multiplication operation.  
Lung Parenchyma Segmentation  
To minimize computational complexity, the data is first converted to grayscale and normalized. [PERSON]'s thresholding method is then applied to binarize the grayscale images. A combined strategy, utilizing morphological opening and area thresholding, is employed to remove noise. The internal cavities within the lung parenchyma are filled twice consecutively to address partially filled or discontinuous regions, resulting in a precise lung parenchyma mask. Next, the left and right lungs are separated through opening operations and pixel value labeling. Several operations are applied to each lung, including mask inversion, morphological opening, inversion, image binarization, hole filling, erosion, and convex hull extraction (the smallest convex polygon). These steps help eliminate interference from small blood vessels, bronchi, and nodule artifacts while smoothing the lung parenchyma boundaries and restoring any missing regions. Finally, the left and right lung masks are merged and binarized to create a complete lung mask. This mask is then multiplied by the original grayscale image to obtain the segmented lung parenchyma, as shown in Figure 3a.  
Consensus Merging of Label Coordinates  
The scan data for each patient is first retrieved using their patient ID and then converted into volumetric data, representing the medical image as a three-dimensional matrix. The contour coordinates of the lung nodules, annotated by four experts as bounding polygons, are then extracted. A consensus merging process is applied at a 50% agreement level, as shown in Figure 3b. This approach combines the manually annotated regions from the four experts, effectively reducing errors caused by individual biases. The resulting merged nodule contours are used to segment the lung parenchyma image, generating the nodule region image.  
ROI Extraction and Data Augmentation  
The ROI is obtained by multiplying the grayscale lung mask image with the binary image of the nodule region from the same slice, as shown in Figure 3c. This process  
Figure 3: Data preprocessing. (**a**) LIDC-0037; (**b**) left: LIDC-0001; right: LIDC-1007; (**c**) LIDC-0001.  
removes interfering elements, yielding a clean and representative lung parenchyma image. To further augment the dataset, random rotations at varying angles are applied to the ROI, ensuring that each data point is viewed from multiple orientations. This augmentation step helps mitigate incidental errors, resulting in a robust final preprocessed dataset.