A fast and adaptive automated disease diagnosis method with an innovative neural network model

Neural Networks

Volumn 33, Issue , 2012, Pages 88-96

  Alkim, Erdem ^a   Gürbüz, Emre ^a   Kiliç, Erdal ^a  

^a ONDOKUZ MAYIS UNIVERSITY   (Turkey)

Author keywords

Adaptive learning vector quantization with reinforcement mechanism; Artificial intelligence; Diagnosis; Diseases

Indexed keywords

ADAPTIVE STRUCTURE; BREAST CANCER; CLASSIFICATION ABILITY; CLASSIFICATION ALGORITHM; CLASSIFICATION TECHNIQUE; DECISION TIME; DEVELOPMENT PROCESS; DISEASE DIAGNOSIS; LEARNING VECTOR QUANTIZATION; LVQ NETWORKS; MISSING VALUES; NEURAL NETWORK MODEL; NUMBER OF ITERATIONS; REINFORCEMENT MECHANISMS; THYROID DISORDERS;

ARTIFICIAL INTELLIGENCE; DISEASES; NEURAL NETWORKS; REINFORCEMENT LEARNING; VECTOR QUANTIZATION;

DIAGNOSIS;

ADAPTIVE LEARNING VECTOR QUANTIZATION WITH REINFORCEMENT MECHANISM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CLASSIFICATION; CLASSIFICATION ALGORITHM; CLASSIFIER; COMPUTER ASSISTED DIAGNOSIS; PRIORITY JOURNAL; THYROID DISEASE;

BREAST NEOPLASMS; DATABASES, FACTUAL; FEMALE; HUMANS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; THYROID DISEASES; TIME FACTORS;

EID: 84863874926     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.04.010     Document Type: Article

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