This paper describes the results after using an automatic classification method to help improve the retrieval of medical images. Using a large dataset of medical images, we established links between low-level features from medical images and high-level features from textual codes of Image Retrieval for Medical Application (IRMA). This paper also explains the process and methods used to automati...

An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low-level features extracted from images and high level knowledge from specia...

In recent years, the application of federated learning to medical image classification has received much attention and achieved some results in study semi-supervised problems, but there are problems such as lack thorough labeled data, serious model degradation case small batches face data category imbalance problem. this paper, we propose a method using combination regularization constraints ps...

The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., development from benign to malignant tumor, CE loss cannot take into account such information allow for better generalization. To improve model gen...