## 5 Conclusions  
In this paper, a study on a health risk diagnosis model based on multi-model image channel expansion and visual pattern standardization algorithm was performed. The proposed method improves the detection model's performance by standardizing the breast ultrasound image's brightness range and diagnosing breast cancer by reducing the lesion image data extracted by the detection model through dimensionality reduction and using it as the input to the RNN model. In breast ultrasound images, the lesion boundaries are not clear due to high brightness noise. We use a channel expansion to reduce such noises and enhance the image. An ultrasound image is an image composed only of brightness values, and removing strong noises can have an adverse effect by breaking down the boundaries of the lesion. Therefore, we introduce removing noise while enhancing the boundaries by repeating noise removal through three normalization processes and combining the three calculated images to expand the channel. The image with the expanded channel improved the detection performance through the YOLOv4 model. The detection model proposed in this paper showed the highest accuracy at 97.34% and showed stable performance with a precision of 97.2%, recall of 97.4%, and F1 score of 94.9%. This paper uses the RNN model to diagnose the lesion image extracted through this detection model. The RNN model creates an ordered matrix format from the data processed through the dimensionality reduction algorithm. The dimensionality reduction algorithm shown in this paper uses a detection filter having a \(\mathbf{2^{n}}\) coefficient to convert visual information into a series of numeric patterns and creates a unique number corresponding to the visual information. It facilitates easy aggregation of visual information and normalization of visual information in a numeric format by identifying the number of the same visual information. This process can transform an image into a \(\mathbf{16\times 16}\) ordered matrix. The transformed data determine what type of cancer the detected lesion is through the RNN model. The RNN model's performance in diagnosing breast cancer was higher than comparative models, with an accuracy of 97.34% and sensitivity of 97.1%. Although the preprocessing method proposed in this paper can strengthen the boundaries inside the image, it did not find a feature that can flawlessly classify \"benign\" and \"malignant\". The pre-processing method proposed in this study can improve the borderline in the image; however, no features that could be used to classify \"benign\" and \"malignant\" were discovered. In the future, a study on pre-processing will be conducted to discover the above characteristics through other studies. Furthermore, by improving the process of normalizing visual information more precisely, the model will be investigated, which can also demonstrate high performance as a model.  
In addition, this study will further enhance the performance through the optimization task for the algorithm proposed in this study and the weight lightening of the model and improve the model, allowing it to detect tumors instead of the classification of tumors for the diagnosis of breast cancer. In addition, this study will develop a comprehensive diagnostic system that will make use of medical data on diseases, such as skin diseases and vascular diseases, for which it is necessary to analyze the shapes in the diagnosis.  
[PERSON]-[PERSON] and [PERSON] conceived and designed the framework; [PERSON] and [PERSON] performed the experiments and analyzed the results. All authors contributed to writing and proofreading the paper. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 2 ICTAP-C157011-02).