Automatic medical image analysis (eg, medical image classification) is widely used in the
early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable …

The automatic and accurate analysis of medical images (eg, segmentation, detection,
classification) are prerequisites for modern disease diagnosis and prognosis. Computer …

Convolutional neural network (CNN) has promoted the development of diagnosis
technology of medical images. However, the performance of CNN is limited by insufficient …

The multi-modality characteristic of medical images calls for the application of information
fusion theory in computer aided diagnosis (CAD) algorithm design. Recently, the research of …

The classification of medical images is an essential task in computer-aided diagnosis,
medical image retrieval and mining. Although deep learning has shown proven advantages …

Automated examination of biomedical signals plays a vital role to diagnose diseases and
offers useful data to several applications in the areas of physiology, sports medicine, and …

Effective fusion of global and local multi-scale features is crucial for medical image
classification. Medical images have many noisy, scattered features, intra-class variations …

Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …

Medical image classification is an important task in computer-aided diagnosis systems. Its
performance is critically determined by the descriptiveness and discriminative power of …

This paper presents a system for medical image diagnosis that uses transfer learning (TL)
and feature selection techniques. The main aim of TL on pre-trained models such as …