Abstract
Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. This paper presents three efficient anomaly detection approaches for applications related to Electroencephalogram (EEG) signal processing and retinal image processing. The first approach depends on the utilization of Scale-Invariant Feature Transform (SIFT) for automatic seizure detection. The second one is based on the utilization of digital filters in a statistical framework for seizure prediction. Finally, an automated Diabetic Retinopathy (DR) diagnosis approach is presented based on the segmentation and detection of anomalous objects from retinal images. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.
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Statistics on Avoidable Blindness; The Initiative for the Elimination of Avoidable Blindness, Vision 2020, a Joint Program of the World Health Organization (WHO) and the International Agency for the Prevention of Blindness (IAPB). Retrieved May 25, 2016 from http://www.vision2020.org/main.cfm?type=WIBDIEBETIC.
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Sedik, A., Emara, H.M., Hamad, A. et al. Efficient anomaly detection from medical signals and images. Int J Speech Technol 22, 739–767 (2019). https://doi.org/10.1007/s10772-019-09610-z
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DOI: https://doi.org/10.1007/s10772-019-09610-z