Version 1
: Received: 3 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (08:48:04 CET)
How to cite:
Bidabad, B.; Nazem, M.; Aghakhani, N. Application of Artificial Intelligence in Intelligent Identification Using Neural Networks in Brain Diseases. Preprints2024, 2024021102. https://doi.org/10.20944/preprints202402.1102.v1
Bidabad, B.; Nazem, M.; Aghakhani, N. Application of Artificial Intelligence in Intelligent Identification Using Neural Networks in Brain Diseases. Preprints 2024, 2024021102. https://doi.org/10.20944/preprints202402.1102.v1
Bidabad, B.; Nazem, M.; Aghakhani, N. Application of Artificial Intelligence in Intelligent Identification Using Neural Networks in Brain Diseases. Preprints2024, 2024021102. https://doi.org/10.20944/preprints202402.1102.v1
APA Style
Bidabad, B., Nazem, M., & Aghakhani, N. (2024). Application of Artificial Intelligence in Intelligent Identification Using Neural Networks in Brain Diseases. Preprints. https://doi.org/10.20944/preprints202402.1102.v1
Chicago/Turabian Style
Bidabad, B., Mobin Nazem and Nozar Aghakhani. 2024 "Application of Artificial Intelligence in Intelligent Identification Using Neural Networks in Brain Diseases" Preprints. https://doi.org/10.20944/preprints202402.1102.v1
Abstract
The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.
Keywords
artificial intelligence; functional neural connectivity; functional magnetic resonance imaging (fMRI); deep learning; neural network; epilepsy; parkinson; seizures
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
