A new semi-supervised approach for characterizing the Arabic on-line handwriting of Parkinson's disease patients.
Alae AmmourIbtissame AouragheGhizlane KhaissidiMostafa MrabtiGhita AboulemFaouzi BelahsenPublished in: Comput. Methods Programs Biomed. (2020)
Keyphrases
- handwritten documents
- multiple sclerosis
- medical doctors
- early diagnosis
- handwriting recognition
- patient groups
- amyotrophic lateral sclerosis
- arabic handwriting recognition
- diagnostic tool
- clinical studies
- emergency department
- handwritten words
- chronic obstructive pulmonary disease
- disease progression
- deep brain stimulation
- cardiovascular disease
- clinically relevant
- chronic disease
- cancer patients
- writer identification
- diabetic patients
- clinical data
- liver disease
- printed text
- clinical trials
- medical treatment
- arabic language
- diabetes mellitus
- handwritten characters
- disease diagnosis
- health care
- medical practice
- medical practitioners
- word recognition
- health status
- medical data
- risk factors
- breast cancer patients
- character recognition
- patient data
- colorectal cancer
- infectious disease
- lung cancer patients
- intraoperative
- caudate nucleus
- breast cancer
- mr images
- medical experts
- signature verification
- document image analysis
- magnetic resonance images
- printed documents
- white matter
- speech recognition