Mammography is a very efficient medical imaging procedure that is used to detect and diagnose breast cancer. However, the use of mammography for the early detection and identification of cancer is very complicated and represents a considerable workload for radiologists. Machine learning (ML) can help address these challenges by providing accurate, automated diagnosis, but traditional ML methods are complex and resourceintensive. Google AutoML Vision offers a simplified approach, enabling healthcare professionals with minimal programming skills to develop effective diagnostic models. The aim of this study was to evaluate the ability of automated deep learning using mammography images using Google AutoML with different augmentation cases. In this work, two models were created: one for binary classification and another for multiclassification. The binary classification model includes two scenarios: noncancerous and malignant, while the multi-classification approach includes three scenarios: normal, benign and malignant. The average accuracy of the two classifications was evaluated and compared. The average accuracy of the binary and multi-classification models was 77.98% and 79.29%, respectively. These results suggest that Google AutoML can simplify the use of ML models in the clinical setting and provide a reliable diagnostic tool that can reduce the workload of radiologists. This study shows that AutoML has the potential to streamline diagnostic workflows in healthcare and make machine learning more accessible and effective in medical practise.OPEN ACCESS Received: 01/08/2024 Accepted: 29/11/2024
Abstract Mammography is a very efficient medical imaging procedure that is used to detect and diagnose breast cancer. However, the use of mammography for the early detection and identification [...]
Breast cancer is one of the leading causes of death in women worldwide and early detection is critical to improving survival rates. In this study, we present a modified deep learning method for automatic feature detection for breast mass classification on mammograms. We propose to use EfficientNet, a Convolutional Neural Network (CNN) architecture that requires minimal parameters. The main advantage of EfficientNet is the small number of parameters, which allows efficient and accurate classification of mammogram images. Our experiments show that EfficientNet, with an overall accuracy of 86.5 percent, has the potential to be the basis for a fully automated and effective breast cancer detection system in the future. Our results demonstrate the potential of EfficientNet to improve the accuracy and efficiency of breast cancer detection compared to other approaches.
Abstract Breast cancer is one of the leading causes of death in women worldwide and early detection is critical to improving survival rates. In this study, we present a modified deep [...]
Breast cancer is the most common cancer diagnosed in women, and it is ranked as the second highest cancer with high mortality rate. Breast-cancer recurrence is the cancerous tumor that returned after treatment. Cancer treatments such as radiotherapy are performed mainly to kill cancer cells; however, some cells may have survived and multiply themselves at the same area as the original cancer (local recurrence) or to any other part (distant recurrence). Distant recurrence occurs when cancer cells spread to other parts of the body, most commonly to bone, breast, liver, and lungs. This study employed an Artificial Neural Network of the deep learning approach to predict distant recurrence of breast cancer. Factors that contribute to the risk of recurrence are age, type of surgery performed, tumor size, breast subtype, estrogen receptor, progesterone receptor, undergoing chemotherapy or not, and lymph node involvement. The actual value of distant recurrence is also considered to be a variable. Principal Component Analysis using five and three principal components was conducted. The outcome indicates that the model has accuracy of up to 0.80 using three principal components.
Abstract Breast cancer is the most common cancer diagnosed in women, and it is ranked as the second highest cancer with high mortality rate. Breast-cancer recurrence is the cancerous [...]
Binary and Multi-Classification Models for Breast Cancer Diagnosis Using Automated Deep Learning and Mammography Images with Different Augmentation Cases 
