Abstract
Computer-Aided Diagnosis (CAD) can improve the accuracy of diagnosis effectively, reduce the rate of misdiagnosis, and provide the support for the valid decision. In clinical applications, high requirements are often imposed on the execution speed and accuracy of CAD systems. The classifier is regarded as the core of the CAD system, that is, the performance of the classifier will have a decisive influence on the operating affection of the CAD system. Extreme Learning Machine (ELM) is a fast learning algorithm using Single Hidden Layer Feedforward Neural Network (SLFN) structure. With its advantages in training speed, generalization performance and accuracy, ELM has draw attention in many research fields, including the development of CAD system. The applications of ELM in CAD are reviewed in this research. First, the mathematical model of ELM and framework of CAD system are briefly introduced. Then, the application of ELM in CAD is reviewed in detail, including the feature modeling method combined with ELM in CAD and the specific application of ELM. Finally, we summarized the current research status of CAD systems based on ELM, and the future work is prospected.
Highlights
Since the last century, with the continuous efforts of researchers in various fields, our knowledge of human anatomy and physiology has grown significantly
Computer-Aided Diagnosis (CAD) refers to the combination of imaging, medical image processing technology and other possible physiological and biochemical methods with computer analysis and calculation, which is used to assist in the detection of lesions or the classification of benign and malignant diseases [5].Through the objective judgment provided by CAD, it plays an active role in improving the efficiency of doctors, the accuracy of diagnosis, reducing the rate of misdiagnose and so on
The CAD system based on the medical image can be divided into two categories: one is the Computer-Aided Detection (CADe) system which detects and locates anomalies on medical images; the other is the Computer-Aided Diagnosis (CADx) system which detects anomalies on medical images and helps doctors determine the types of anomalies and malignant levels
Summary
With the continuous efforts of researchers in various fields, our knowledge of human anatomy and physiology has grown significantly. In 1963, Lodwick et al [4]proposed the method of digitizing X-ray films This provides a practical foundation for the use of computers to extract multidimensional information from medical data to assist doctors in diagnosis. CAD refers to the combination of imaging, medical image processing technology and other possible physiological and biochemical methods with computer analysis and calculation, which is used to assist in the detection of lesions or the classification of benign and malignant diseases [5].Through the objective judgment provided by CAD, it plays an active role in improving the efficiency of doctors, the accuracy of diagnosis, reducing the rate of misdiagnose and so on. In order to solve the classification problem of non-equilibrium data, Cao et al [12] proposed Weighted Extreme Learning Machine (WELM).
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