A Novel Medical Decision-Making System Based on Multi-Scale Feature Enhancement for Small Samples

• Main Authors: Gou, F. (Author), He, K. (Author), Qin, Y. (Author), Wu, J. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: decision-making system; few-shot; imaging segmentation; multi-scale feature enhancement
• Online Access: View Fulltext in Publisher; View in Scopus

 The medical decision-making system is an advanced system for patients that can assist doctors in their medical work. Osteosarcoma is a primary malignant tumor of the bone, due to its specificity, such as its blurred borders, diverse tumor morphology, and inconsistent scales. Diagnosis is quite difficult, especially for developing countries, where medical resources are inadequate per capita and there is a lack of professionals, and the time spent in the diagnosis process may lead to a gradual deterioration of the disease. To address these, we discuss an osteosarcoma-assisted diagnosis system (OSADS) based on small samples with multi-scale feature enhancement that can assist doctors in performing preliminary automatic segmentation of osteosarcoma and reduce the workload. We proposed a multi-scale feature enhancement network (MFENet) based on few-shot learning in OSADS. Global and local feature information is extracted to effectively segment the boundaries of osteosarcoma by feeding the images into MFENet. Simultaneously, a prior mask is introduced into the network to help it maintain a certain accuracy range when segmenting different shapes and sizes, saving computational costs. In the experiments, we used 5000 osteosarcoma MRI images provided by Monash University for testing. The experiments show that our proposed method achieves 93.1% accuracy and has the highest comprehensive evaluation index compared with other methods. © 2023 by the authors.