The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combin...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2022-01-01.
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| Online Access: | Get fulltext |
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| LEADER | 02681 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs25706_15906 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Ahmed, Ahmed Shihab |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Salah, Hussein Ali |e author |
| 245 | 0 | 0 | |a The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network |
| 260 | |b Institute of Advanced Engineering and Science, |c 2022-01-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25706 | ||
| 520 | |a The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, "in the eighth one," the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Arduino global system for mobile; Convolution neural network; Discrete wavelet transform; Genetic algorithm; Internet of things; Registration of magnetic resonance imaging brain | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 25, No 1: January 2022; 273-280 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v25.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25706/15906 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25706/15906 |z Get fulltext |