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Effective Evaluation of Medical Images Using Artificial Intelligence Techniques.
Kannan, S; Premalatha, G; Jamuna Rani, M; Jayakumar, D; Senthil, P; Palanivelrajan, S; Devi, S; Sahile, Kibebe.
Afiliación
  • Kannan S; Department of Electronics and Communication Engineering, Mallareddy Institute of Technology and Science, Secunderabad 500100, Telangana, India.
  • Premalatha G; Department of Computer Science and Engineering, Mohamed Sathak A. J College of Engineering, Sipcot IT Park, Siruseri, Chennai 603103, Tamilnadu, India.
  • Jamuna Rani M; Department of Electronics and Communication Engineering, Sona College of Technology, Salem 636005, Tamilnadu, India.
  • Jayakumar D; Department of Electronics and Communication Engineering, Kuppam Engineering College, Kuppam 517425, Andhra Pradesh, India.
  • Senthil P; Department of Electronics and Communication Engineering, SRM Institute of Science & Technology, Ramapuram, Chennai 603203, Tamilnadu, India.
  • Palanivelrajan S; Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Thalavapalayam, Karur 639113, Tamilnadu, India.
  • Devi S; Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Nellore, Andhra Pradesh-52316, India.
  • Sahile K; Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Comput Intell Neurosci ; 2022: 8419308, 2022.
Article en En | MEDLINE | ID: mdl-35990128
This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson's disease, the affected patients with Parkinson's disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Inteligencia Artificial Tipo de estudio: Diagnostic_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Límite: Humans Idioma: En Revista: Comput Intell Neurosci Asunto de la revista: INFORMATICA MEDICA / NEUROLOGIA Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Inteligencia Artificial Tipo de estudio: Diagnostic_studies / Prognostic_studies / Qualitative_research / Risk_factors_studies Límite: Humans Idioma: En Revista: Comput Intell Neurosci Asunto de la revista: INFORMATICA MEDICA / NEUROLOGIA Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Estados Unidos