FUSING ULTRASOUND AND MAMMOGRAPHY IMAGES: A NOVEL AI APPROACH TO IMPROVE BREAST CANCER SCREENING AND DIAGNOSIS

OBJECTIVE The primary Objective: of this research is to develop and evaluate cross-image and fusion artificial intelligence methods that can improve the recognition speed and screening rate of breast cancer in B-mode ultrasound and mammography images, potentially leading to more accurate and timely diagnoses for women at risk. MATERIALS & METHODS Data was collected from public databases, such as RSNA (Radiological Society of North America), PAS (Polish Academy of Sciences), and the Kaggle website, ensuring a diverse set of B-mode ultrasound images and mammography images. The images were divided into two groups representing malignant and benign tumors. The data augmentation techniques were applied to increase the number of ultrasound image samples. Model 1 is uses convolutional neural networks (CNN) to extract features from ultrasound and X-ray images separately, and then integrate these features to build a machine learning classification model. Model 2 is user designed CNN model to classify simultaneously incorporate ultrasound and X-ray image. The hyperparameters included batch size, epoch size, and learning rates. Performance indices included recall rate, precision, accuracy and Kappa value. RESULTS The accuracy and Kappa values were 0.884 and 0.766 generated by Model 1. Meanwhile, the accuracy and Kappa values were 0.964 and 0.923 provided by the Model 2. CONCLUSION The integration of ultrasound and X-ray images using a CNN classification model, as seen in Model 2, proved to be an effective approach for improving the recognition speed and screening rate of breast cancer. The primary Objective: of this research is to develop and evaluate cross-image and fusion artificial intelligence methods that can improve the recognition speed and screening rate of breast cancer in B-mode ultrasound and mammography images, potentially leading to more accurate and timely diagnoses for women at risk. Data was collected from public databases, such as RSNA (Radiological Society of North America), PAS (Polish Academy of Sciences), and the Kaggle website, ensuring a diverse set of B-mode ultrasound images and mammography images. The images were divided into two groups representing malignant and benign tumors. The data augmentation techniques were applied to increase the number of ultrasound image samples. Model 1 is uses convolutional neural networks (CNN) to extract features from ultrasound and X-ray images separately, and then integrate these features to build a machine learning classification model. Model 2 is user designed CNN model to classify simultaneously incorporate ultrasound and X-ray image. The hyperparameters included batch size, epoch size, and learning rates. Performance indices included recall rate, precision, accuracy and Kappa value. The accuracy and Kappa values were 0.884 and 0.766 generated by Model 1. Meanwhile, the accuracy and Kappa values were 0.964 and 0.923 provided by the Model 2. The integration of ultrasound and X-ray images using a CNN classification model, as seen in Model 2, proved to be an effective approach for improving the recognition speed and screening rate of breast cancer.