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A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms Noor Salah Hassan & Nawzat Sadiq Ahmed

A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms

Author (s)

Noor Salah Hassan & Nawzat Sadiq Ahmed

Abstract

­

The detection of the tumor region in medical images such as (MRI, CT, X-Ray) a boring and time-consuming task is by radiologists or experts. So, in this review the accuracy is needed for detection the tumor. The area of medical imaging is also reducing complexity and improving diagnostic precision with the growth of information technology. This review paper makes a comparison between the k-mean, and fuzzy c-mean algorithms to display the results and accuracy of them to detection the brain tumor. The execution of the k-mean algorithm is based on centroid, size, split process, threshold, epoch, characteristics, and number of iterations, while Fuzzy C-mean is executed on the basis of the fuzziness value and the termination condition in medical images. In comparing the efficiency parameters with the state-of-the-art processes, the experimental outcomes demonstrate the importance of medical images (MRI, CT and X-Ray) and the accuracy of each algorithm that have been discussed.

 Keywords: Brain Tumor, Medical Images, Pre-processing, Segmentation, Feature Extraction, K- means.

 

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Title: A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms
Author: Noor Salah Hassan & Nawzat Sadiq Ahmed
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4596362
Media: Online
Volume: 5
Issue: 6
Acceptance Date: 07/03/2021
Date of Publication: 11/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/743.pdf
Free download: Available
Page: 21-32
First Page: 21
Last Page: 32
Paper Type: Literature review
Current Status: Published

 

Cite This Article:

Noor Salah Hassan & Nawzat Sadiq Ahmed (2021). A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms. International Journal of Science and Business, 5(6), 21-32. doi: https://doi.org/10.5281/zenodo.4596362

Retrieved from https://ijsab.com/wp-content/uploads/743.pdf

 

About Author (s)

Noor Salah Hassan (corresponding author), Department of Information Technology, Akre Technical Collage, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email:  noor.salah.hassan6@gmail.com

Nawzat Sadiq Ahmed, Department of Information Technology, Technical Collage of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email:  nawzat.ahmed@dpu.edu.krd.

 

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DOI: https://doi.org/10.5281/zenodo.4596362

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Deep Learning Convolutional Neural Network for Face Recognition: A Review Rondik J. Hassan & Adnan Mohsin Abdulazeez

Deep Learning Convolutional Neural Network for Face Recognition:

A Review

Author (s)

Rondik J. Hassan & Adnan Mohsin Abdulazeez

Abstract

­Face recognition is increasingly being used for solving various social-problems such as personal protection and authentication. As with other widely used biometric applications, facial recognition is a biometric instrument such as iris recognition, vein pattern recognition, and fingerprint recognition. Facial recognition identifies a person based on certain aspects of his physiology. Deep Learning (DL) is a branch of machine learning (ML) that can be used in image processing and pattern recognition to solve multiple problems, one of the applications is face recognition. With the advancement of deep learning, Convolution Neural Network (CNN) based facial recognition technology has been the dominant approach adopted in the field of face recognition. The purpose of this paper is to provide a review of face recognition approaches. Furthermore, the details of each paper, such as used datasets, algorithms, architecture, and achieved results are summarized and analyzed comprehensively.

 Keywords: Face Recognition, Machine Learning, Deep Learning, Convolution Neural Network, Feature Extraction, Feature Matching.

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Title: Deep Learning Convolutional Neural Network for Face Recognition: A Review
Author: Rondik J. Hassan & Adnan Mohsin Abdulazeez
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4471013
Media: Online
Volume: 5
Issue: 2
Acceptance Date: 24/01/2021
Date of Publication: 27/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/675.pdf
Free download: Available
Page: 114-127
First Page: 114
Last Page: 127
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Rondik J. Hassan & Adnan Mohsin Abdulazeez (2021). Deep Learning Convolutional Neural Network for Face Recognition: A Review, International Journal of Science and Business, 5(2), 114-127. doi: https://doi.org/ 10.5281/zenodo.4471013

Retrieved from https://ijsab.com/wp-content/uploads/675.pdf

 

About Author (s)

Rondik J.Hassan (corresponding author), Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.  E-mail: rondik.jamaluddin@gmail.com

Professor Adnan Mohsin Abdulazeez, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. E-mail: adnan.mohsin@dpu.edu.krd

 

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DOI: https://doi.org/10.5281/zenodo.4471013