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

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

PDF - Published Version Restricted to Repository staff only Download (236kB) | Request a copy

Abstract

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.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Deep learning, Convolutional Neural Network (CNN), VGG-16, medical image classification.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	17 Dec 2024 16:44
Last Modified:	17 Dec 2024 16:44
URI:	http://irep.iium.edu.my/id/eprint/116736

Actions (login required)

View Item