The Utilising VGG-16 of Convolutional Neural Network for Medical Image Classification
DOI:
https://doi.org/10.31436/ijpcc.v10i1.460Keywords:
Deep learning, Convolutional Neural Network (CNN), VGG-16, medical image classificationAbstract
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.
References
M. C. Mabray, R. F. Barajas, & S. Cha, “Modern Brain Tumor Imaging”. Brain Tumor Research and Treatment, 2015. https://synapse.koreamed.org/articles/1059671
S. Roy, S. Nag, I. K. Maitra, & S. K. Bandyopadhyay, “A review on Automated Brain Tumor Detection and segmentation from MRI of brain”. NASA/ADS, 2013. https://ui.adsabs.harvard.edu/abs/2013arXiv1312.6150R/abstract
M. To?açar, B. Ergen, & Z. Cömert, “BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model”. Medical Hypotheses, 134, 109531, 2020. https://doi.org/10.1016/j.mehy.2019.109531
K. Sharma, A. Kaur, & S. Gujral, “Brain Tumor Detection based on Machine Learning Algorithms”. International Journal of Computer Applications, 103(1), 7–11, 2014. https://doi.org/10.5120/18036-6883
M. S. I. Khan, A. Rahman, T. Debnath, M. R. Karim, M. K. Nasir, S. S. Band, A. Mosavi, & I. Dehzangi, “Accurate brain tumor detection using deep convolutional neural network”. Computational and Structural Biotechnology Journal, 20, 4733–4745, 2022. https://doi.org/10.1016/j.csbj.2022.08.039
A. Chattopadhyay, & M. Maitra, “MRI-based brain tumour image detection using CNN based deep learning method”. Neuroscience informatics, 2(4), 100060, 2022.
A. K. Sekuboyina, S. T. Devarakonda and C. S. Seelamantula, "A convolutional neural network approach for abnormality detection in Wireless Capsule Endoscopy," IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia, 2017, pp. 1057-1060, doi: 10.1109/ISBI.2017.7950698.
M. Hajabdollahi, R. Esfandiarpoor, E. Sabeti, N. Karimi, S.M.R. Soroushmehr, S. Samavi, “Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network”. Biomedical Signal Processing and Control, 57, 101792, 2020. https://doi.org/10.1016/j.bspc.2019.101792.
L. Gaur, U. Bhatia, N. Z. Jhanjhi, G. Muhammad, & M. Masud, “Medical image-based detection of COVID-19 using deep convolution neural networks”. Multimedia systems, 29(3), 1729-1738, 2023. https://doi.org/10.1007/s00530-021-00794-6
J. Wu, N. Liu, X. Li, Q. Fan, Z. Li, J. Shang, F. Wang, B. Chen, Y. Shen, P. Cao, & Z. Liu, “Convolutional neural network for detecting rib fractures on chest radiographs: a feasibility study”. BMC Medical Imaging, 23(1), pp.1-12, 2023. https://doi.org/10.1186/s12880-023-00975-x
