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Utilising VGG-16 of convolutional neural network for medical image classification

Medical image classification, which involves accurately classifying anomalies or abnormalities within images, is an important area of attention in healthcare domain. It requires a fast and exact classification to ensure appropriate and timely treatment to the patients. This paper introduces a model...

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Main Authors: Ismail, Amelia Ritahani, Nisa, Syed Qamrun, Shaharuddin, Shahida Adila, Masni, Syahmi Irdina, Suharudin Amin, Syaza Athirah
Format: Article
Language:English
Published: IIUM Press 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/116736/7/116736_%20Utilising%20VGG-16%20of%20convolutional.pdf
http://irep.iium.edu.my/116736/
https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/460/279
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spelling my.iium.irep.1167362024-12-17T08:44:44Z http://irep.iium.edu.my/116736/ Utilising VGG-16 of convolutional neural network for medical image classification Ismail, Amelia Ritahani Nisa, Syed Qamrun Shaharuddin, Shahida Adila Masni, Syahmi Irdina Suharudin Amin, Syaza Athirah QA75 Electronic computers. Computer science Medical image classification, which involves accurately classifying anomalies or abnormalities within images, is an important area of attention in healthcare domain. It requires a fast and exact classification to ensure appropriate and timely treatment to the patients. This paper introduces a model based on Convolutional Neural Network (CNN) that utilises the VGG16 architecture for medical image classification, specifically in brain tumour and Alzheimer dataset. The VGG16 architecture, is known for its remarkable ability to extract important features, that is crucial in medical image classification. To enhance the precision of diagnosis, a detailed experimental setup is conducted, which includes the careful selection and organisation of a collection of medical images that cover different illnesses and anomalies to the dataset. The architecture of the model is then adjusted to achieve optimal performance in for image classification. The results show the model's efficiency in identifying anomalies in medical images especially for brain tumour dataset. The sensitivity, specificity, and F1-score evaluation metrics are presented, emphasising the model's ability to accurately differentiate between various medical image diseases. IIUM Press 2024-01-28 Article PeerReviewed application/pdf en http://irep.iium.edu.my/116736/7/116736_%20Utilising%20VGG-16%20of%20convolutional.pdf Ismail, Amelia Ritahani and Nisa, Syed Qamrun and Shaharuddin, Shahida Adila and Masni, Syahmi Irdina and Suharudin Amin, Syaza Athirah (2024) Utilising VGG-16 of convolutional neural network for medical image classification. International Journal on Perceptive and Cognitive Computing (IJPCC), 10 (1). pp. 113-118. E-ISSN 2462-229X https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/460/279 10.31436/ijpcc.v10i1.460
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ismail, Amelia Ritahani
Nisa, Syed Qamrun
Shaharuddin, Shahida Adila
Masni, Syahmi Irdina
Suharudin Amin, Syaza Athirah
Utilising VGG-16 of convolutional neural network for medical image classification
description Medical image classification, which involves accurately classifying anomalies or abnormalities within images, is an important area of attention in healthcare domain. It requires a fast and exact classification to ensure appropriate and timely treatment to the patients. This paper introduces a model based on Convolutional Neural Network (CNN) that utilises the VGG16 architecture for medical image classification, specifically in brain tumour and Alzheimer dataset. The VGG16 architecture, is known for its remarkable ability to extract important features, that is crucial in medical image classification. To enhance the precision of diagnosis, a detailed experimental setup is conducted, which includes the careful selection and organisation of a collection of medical images that cover different illnesses and anomalies to the dataset. The architecture of the model is then adjusted to achieve optimal performance in for image classification. The results show the model's efficiency in identifying anomalies in medical images especially for brain tumour dataset. The sensitivity, specificity, and F1-score evaluation metrics are presented, emphasising the model's ability to accurately differentiate between various medical image diseases.
format Article
author Ismail, Amelia Ritahani
Nisa, Syed Qamrun
Shaharuddin, Shahida Adila
Masni, Syahmi Irdina
Suharudin Amin, Syaza Athirah
author_facet Ismail, Amelia Ritahani
Nisa, Syed Qamrun
Shaharuddin, Shahida Adila
Masni, Syahmi Irdina
Suharudin Amin, Syaza Athirah
author_sort Ismail, Amelia Ritahani
title Utilising VGG-16 of convolutional neural network for medical image classification
title_short Utilising VGG-16 of convolutional neural network for medical image classification
title_full Utilising VGG-16 of convolutional neural network for medical image classification
title_fullStr Utilising VGG-16 of convolutional neural network for medical image classification
title_full_unstemmed Utilising VGG-16 of convolutional neural network for medical image classification
title_sort utilising vgg-16 of convolutional neural network for medical image classification
publisher IIUM Press
publishDate 2024
url http://irep.iium.edu.my/116736/7/116736_%20Utilising%20VGG-16%20of%20convolutional.pdf
http://irep.iium.edu.my/116736/
https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/460/279
_version_ 1818833733561614336
score 13.223943

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