A Study of Neutrosophic Sets and Deep Learning Models for Breast Cancer Classification

Abstract

Medical image classification and detection using artificial intelligence (AI) can help enhance medical care services. Unfortunately, most medical image modalities suffer from some noise and uncertainty, which decreases the performance of disease detection and classification. Neutrosophic sets (NS) can handle uncertainty data within medical images. NS can present images into three subsets: true (T), indeterminacy (I), and fuzzy sets (FS). In this study, we investigate the performance of deep learning (DL) models under the NS domain for breast cancer classification. The crisp image domain is converted to the NS domain and represents the image into three subsets. The converted images are used to train seven DL models, such as VGG16, VGG19, ResNet50, ResNet150, DenseNest121, EfficientNetB2, and MobileNetV2. A comparison between DL under NS and on the FS, domain has been made in terms of accuracy, precision, recall, and F1 score. The experimental results showed superior results for NS over FS.