Brain tumour classification of Magnetic resonance images using a novel CNN based Medical Image Analysis and Detection network in comparison with VGG16
Main Article Content
Keywords
Brain image classification, Convolutional neural network, deep learning, Brain tumour, Novel Medical Image Analysis and Detection network(MIDNet 18), VGG16
Abstract
Abstract: Aim: This study aims at developing an automatic medical image analysis and detection for
accurate classification of brain tumors from MRI dataset. The study implemented our novel MIDNet18
CNN architecture in comparison with the VGG16 CNN architecture for classifying normal brain images
from the brain tumor images. Materials and methods: The novel MIDNet-18 CNN architecture
comprises 14 convolutional layers, 7 pooling layers, 4 dense layers and 1 classification layer. The dataset
used for this study has two classes: Normal Brain MR Images and Brain Tumor MR Images. This binary
MRI brain dataset consists of 2918 images as training set, 1458 images as validation set and 212 images as
test set. Independent sample size calculated was 7 for each group, keeping GPower at 80%. Result: From
the experimental results, it could be inferred that our novel MIDNet18 was 98% better than VGG16,
which was statistically significant with p value <0.001(Independent sample t-test).
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Artificial Intelligence in Medicine 102 (January): 101779.
Badža, Milica M., and Marko Č. Barjaktarović. 2020. “Classification of Brain Tumors from MRI Images
Using a Convolutional Neural Network.” Applied Sciences. https://doi.org/10.3390/app10061999.
Baranwal, Shubham Kumar, Krishnkant Jaiswal, Kumar Vaibhav, Abhishek Kumar, and R.
Srikantaswamy. 2020. “Performance Analysis of Brain Tumour Image Classification Using CNN and
SVM.” 2020 Second International Conference on Inventive Research in Computing Applications
(ICIRCA). https://doi.org/10.1109/icirca48905.2020.9183023.
“Cancer Statistics.” 2015. April 2, 2015. https://www.cancer.gov/about-cancer/understanding/statistics.
Chakrabarty, Satrajit, Aristeidis Sotiras, Mikhail Milchenko, Pamela LaMontagne, Michael Hileman, and
Daniel Marcus. 2021. “MRI-Based Identification and Classification of Major Intracranial Tumor
Types by Using a 3D Convolutional Neural Network: A Retrospective Multi-Institutional Analysis.”
Radiology. Artificial Intelligence 3 (5): e200301.
“Classification of Brain MRI Tumor Images: A Hybrid Approach.” 2017. Procedia Computer Science
122 (January): 510–17.
Ferlay, Jacques, Murielle Colombet, Isabelle Soerjomataram, Donald M. Parkin, Marion Piñeros, Ariana
Znaor, and Freddie Bray. 2021. “Cancer Statistics for the Year 2020: An Overview.” International
Journal of Cancer. https://doi.org/10.1002/ijc.33588.
Gu, Xiaoqing, Zongxuan Shen, Jing Xue, Yiqing Fan, and Tongguang Ni. 2021. “Brain Tumor MR Image
Classification Using Convolutional Dictionary Learning With Local Constraint.” Frontiers in
Neuroscience 15 (May): 679847.
Hang, Siang Thye, and Masaki Aono. 2017. “Bi-Linearly Weighted Fractional Max Pooling.” Multimedia
Tools and Applications. https://doi.org/10.1007/s11042-017-4840-5.
Irmak, Emrah. 2021. “Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional
Neural Network with Fully Optimized Framework.” Iranian Journal of Science and Technology,
Transactions of Electrical Engineering. https://doi.org/10.1007/s40998-021-00426-9.
Jiang, Zhi-Peng, Yi-Yang Liu, Zhen-En Shao, and Ko-Wei Huang. 2021. “An Improved VGG16 Model
for Pneumonia Image Classification.” Applied Sciences. https://doi.org/10.3390/app112311185.
Jia, Zheshu, and Deyun Chen. 2020. “Brain Tumor Identification and Classification of MRI Images Using
Deep Learning Techniques.” IEEE Access. https://doi.org/10.1109/access.2020.3016319.
Kader, Isselmou Abd El, Isselmou Abd El Kader, Guizhi Xu, Zhang Shuai, and Sani Saminu. 2021.
“Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images.”
Current Medical ImagingFormerly: Current Medical Imaging Reviews.
https://doi.org/10.2174/1573405617666210224113315.
Kang, Jaeyong, Zahid Ullah, and Jeonghwan Gwak. 2021. “MRI-Based Brain Tumor Classification Using
Ensemble of Deep Features and Machine Learning Classifiers.” Sensors 21 (6).
https://doi.org/10.3390/s21062222.
Kaur, Prabhjot, Shilpi Harnal, Rajeev Tiwari, Fahd S. Alharithi, Ahmed H. Almulihi, Irene Delgado
Noya, and Nitin Goyal. 2021. “A Hybrid Convolutional Neural Network Model for Diagnosis of
COVID-19 Using Chest X-Ray Images.” International Journal of Environmental Research and
Public Health 18 (22). https://doi.org/10.3390/ijerph182212191.
Khagi, Bijen, and Goo-Rak Kwon. 2020. “3D CNN Design for the Classification of Alzheimer’s Disease
Using Brain MRI and PET.” IEEE Access. https://doi.org/10.1109/access.2020.3040486.
Morad, Ameer Hussian, and Hadeel Moutaz Al-Dabbas. 2020. “Classification of Brain Tumor Area for
MRI Images.” Journal of Physics. Conference Series 1660 (November): 012059.
Murray, Naila, and Florent Perronnin. 2014. “Generalized Max Pooling.” 2014 IEEE Conference on
Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.317.
Özyurt, Fatih, Eser Sert, Engin Avci, and Esin Dogantekin. n.d. Brain Tumor Detection Based on
Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy. Infinite
Study.
Rajesh, Bulla, Mohammed Javed, Ratnesh, and Shubham Srivastava. 2019. “DCT-CompCNN: A Novel
Image Classification Network Using JPEG Compressed DCT Coefficients.” 2019 IEEE Conference
on Information and Communication Technology. https://doi.org/10.1109/cict48419.2019.9066242.
Ranjbarzadeh, Ramin, Abbas Bagherian Kasgari, Saeid Jafarzadeh Ghoushchi, Shokofeh Anari, Maryam
Naseri, and Malika Bendechache. 2021. “Brain Tumor Segmentation Based on Deep Learning and an
Attention Mechanism Using MRI Multi-Modalities Brain Images.” Scientific Reports 11 (1): 10930.
Roy, Sanjiban Sekhar, Nishant Rodrigues, and Y-H Taguchi. 2020. “Incremental Dilations Using CNN for
Brain Tumor Classification.” Applied Sciences. https://doi.org/10.3390/app10144915.
Sharma, Kirti, Ketna Khanna, Sapna Gambhir, and Mohit Gambhir. 2022. “Study on Brain Tumor
Classification Through MRI Images Using a Deep Convolutional Neural Network.” International
Journal of Information Retrieval Research. https://doi.org/10.4018/ijirr.289610.
“The DCT-CNN-ResNet50 Architecture to Classify Brain Tumors with Super-Resolution, Convolutional
Neural Network, and the ResNet50.” 2021. Neuroscience Informatics 1 (4): 100013.
Zacharaki, Evangelia I., Sumei Wang, Sanjeev Chawla, Dong Soo Yoo, Ronald Wolf, Elias R. Melhem,
and Christos Davatzikos. 2009a. “Classification of Brain Tumor Type and Grade Using MRI Texture
and Shape in a Machine Learning Scheme.” Magnetic Resonance in Medicine.
https://doi.org/10.1002/mrm.22147.
———. 2009b. “MRI-Based Classification of Brain Tumor Type and Grade Using SVM-RFE.” 2009
IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
https://doi.org/10.1109/isbi.2009.5193232
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Published
Jan 29, 2022
Article Details
How to Cite
Ramya Mohan, Kirupa Ganapathy, & Rama A. (2022). Brain tumour classification of Magnetic resonance images using a novel CNN based Medical Image Analysis and Detection network in comparison with VGG16. Journal of Population Therapeutics and Clinical Pharmacology, 28(2). https://doi.org/10.47750/jptcp.2022.873
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How to Cite
Ramya Mohan, Kirupa Ganapathy, & Rama A. (2022). Brain tumour classification of Magnetic resonance images using a novel CNN based Medical Image Analysis and Detection network in comparison with VGG16. Journal of Population Therapeutics and Clinical Pharmacology, 28(2). https://doi.org/10.47750/jptcp.2022.873