Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization

• Published in: Applied sciences 2021-09, Vol.11 (18), p.8621
• Main Authors: Kim, Chang-Min, Hong, Ellen J., Chung, Kyungyong, Park, Roy C.
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Big Data; Breast cancer; Channels; Classification; Deep learning; Diagnosis; Disease; Health risk assessment; Health risks; image channel expansion; Image classification; LFA; Lung cancer; Machine learning; Mammography; Medical diagnosis; Medical equipment; Medical imaging; Medical prognosis; Medical screening; Medical technology; Metastasis; Normalizing; Patients; Physicians; Software; Standardization; Tumors; Ultrasonic imaging; visual pattern standardization; YOLO
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11188621

Although mammography is an effective screening method for early detection of breast cancer, it is also difficult for experts to use since it requires a high level of sensitivity and expertise. A computer-aided detection system was introduced to improve the detection accuracy of breast cancer in mammography, which is difficult to read. In addition, research to find lesions in mammography images using artificial intelligence has been actively conducted in recent days. However, the images generally used for breast cancer diagnosis are high-resolution and thus require high-spec equipment and a significant amount of time and money to learn and recognize the images and process calculations. This can lower the accuracy of the diagnosis since it depends on the performance of the equipment. To solve this problem, this paper will propose a health risk detection and classification model using multi-model-based image channel expansion and visual pattern shaping. The proposed method expands the channels of breast ultrasound images and detects tumors quickly and accurately through the YOLO model. In order to reduce the amount of computation to enable rapid diagnosis of the detected tumors, the model reduces the dimensions of the data by normalizing the visual information and use them as an input for the RNN model to diagnose breast cancer. When the channels were expanded through the proposed brightness smoothing and visual pattern shaping, the accuracy was the highest at 94.9%. Based on the images generated, the study evaluated the breast cancer diagnosis performance. The results showed that the accuracy of the proposed model was 97.3%, CRNN 95.2%, VGG 93.6%, AlexNet 62.9%, and GoogleNet 75.3%, confirming that the proposed model had the best performance.