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For automatic diagnosis, a threshold-based stage using the Pearson product-moment correlation coefficient is presented.
Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones.
However, the environmental influence and image noise hamper the efficiency of automatic diagnosis.
The wavelet transform is an effective tool for extracting discriminative features in ECG signal classification for automatic diagnosis of cardiac arrhythmia.
Automatic diagnosis is one of the most important parts in the expert system of traditional Chinese medicine (TCM), and in recent years, it has been studied widely.
Detecting the locations of the optic disc and fovea is a crucial task towards developing automatic diagnosis and screening tools for retinal disease.
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OBJECTIVE: To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening.
Liu et al. [ 19] developed an automatic glaucoma diagnosis and screening architecture, automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII), which combined subjects' personal data, imaging information from Digital Fundus Photographs (DFPs), and patients' genome information for glaucoma diagnosis.
Despite considerable efforts to defend against exploits, automatic exploit diagnosis remains a significant challenge.
In this study the influence of different stopping criteria on automatic fault diagnosis is investigated.
Experimental results indicate a classification rate of 98% using an SVM classifier which is encouraging for automatic osteoporosis diagnosis using bone X-ray images.
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