Brain tumor classification of magnetic resonance Images using novel CNN-based medical image Analysis and Detection network in comparison with AlexNet Brain tumor classification using novel CNN
Main Article Content
Keywords
Brain tumor image, Binary classification, Convolutional neural network, deep learning, Novel Medical Image Analysis and Detection network (MIDNet18), AlexNet
Abstract
Abstract: Aim: This research work aims in developing an automatic medical image analysis and detection for accurate classification of brain tumors from MRI dataset. The work developed a new MIDNet18 CNN architecture in comparison with the AlexNet 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 performance metrics, it could be inferred that our novel MIDNet18 achieved higher test accuracy, AUC, F1 Score, Precision and Recall over the ALEXNet algorithm. Conclusion: From the result, it could be concluded that the MIDNet18 is significantly more accurate (Independent sample t-test p <0.05) than the AlexNet in classifying the tumors from the Brain MRI images.
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Bondy, Melissa L., Michael E. Scheurer, Beatrice Malmer, Jill S. Barnholtz-Sloan, Faith G. Davis, Dora Il’yasova, Carol Kruchko, et al. 2008. “Brain Tumor Epidemiology: Consensus from the Brain Tumor Epidemiology Consortium.” Cancer 113 (7 Suppl): 1953–68.
Caruso, Gerardo, Lucia Merlo, and Maria Caffo. 2014. “Brief Introduction on Brain Tumor Epidemiology and State of the Art in Therapeutics.” Innovative Brain Tumor Therapy. https://doi.org/10.1533/9781908818744.1.
Consortium, The Childhood Brain Tumor, The Childhood Brain Tumor Consortium, and F. H. Gilles. 1991. “The Epidemiology of Headache among Children with Brain Tumor.” Journal of Neuro-Oncology. https://doi.org/10.1007/bf00151245.
Gab Allah, Ahmed M., Amany M. Sarhan, and Nada M. Elshennawy. 2021. “Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation.” Diagnostics (Basel, Switzerland) 11 (12). https://doi.org/10.3390/diagnostics11122343.
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.
Hossain, Md Jobayer, Wendi Xiao, Maliha Tayeb, and Saira Khan. 2021. “Epidemiology and Prognostic Factors of Pediatric Brain Tumor Survival in the US: Evidence from Four Decades of Population Data.” Cancer Epidemiology. https://doi.org/10.1016/j.canep.2021.101942.
Huang, Jonathan, Nathan A. Shlobin, Sandi K. Lam, and Michael DeCuypere. 2021. “Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review.” World Neurosurgery 157 (October): 99–105.
Ismael, Mustafa Rashid. 2018. Hybrid Model: Statistical Features and Deep Neural Network for Brain Tumor Classification in MRI Images.
Johnson, Kimberly J., Helle Broholm, Michael E. Scheurer, Ching C. Lau, Johannes A. Hainfellner, Joseph Wiemels, and Judith Schwartzbaum. 2018. “Advancing Brain Tumor Epidemiology – Multi-Level Integration and International Collaboration: The 2018 Brain Tumor Epidemiology Consortium Meeting Report.” Clinical Neuropathology. https://doi.org/10.5414/np301148.
“Kaggle: Your Machine Learning and Data Science Community.” n.d. Accessed December 17, 2021. https://www.kaggle.com/.
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.
Kesav, Nivea, and M. G. Jibukumar. 2021. “Efficient and Low Complex Architecture for Detection and Classification of Brain Tumor Using RCNN with Two Channel CNN.” Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.05.008.
Khan, Amjad Rehman, Siraj Khan, Majid Harouni, Rashid Abbasi, Sajid Iqbal, and Zahid Mehmood. 2021. “Brain Tumor Segmentation Using K-Means Clustering and Deep Learning with Synthetic Data Augmentation for Classification.” Microscopy Research and Technique 84 (7): 1389–99.
Kremelberg, David. 2010. Practical Statistics: A Quick and Easy Guide to IBM® SPSS® Statistics, STATA, and Other Statistical Software. SAGE Publications, Incorporated.
Liu, George S., Angela Yang, Dayoung Kim, Andrew Hojel, Diana Voevodsky, Julia Wang, Charles C. L. Tong, et al. 2022. “Deep Learning Classification of Inverted Papilloma Malignant Transformation Using 3D Convolutional Neural Networks and Magnetic Resonance Imaging.” International Forum of Allergy & Rhinology, January. https://doi.org/10.1002/alr.22958.
Lotlikar, Venkatesh S., Nitin Satpute, and Aditya Gupta. 2021. “Brain Tumor Detection Using Machine Learning and Deep Learning: A Review.” Current Medical Imaging Reviews, September. https://doi.org/10.2174/1573405617666210923144739.
Miller, Kimberly D., Quinn T. Ostrom, Carol Kruchko, Nirav Patil, Tarik Tihan, Gino Cioffi, Hannah E. Fuchs, et al. 2021. “Brain and Other Central Nervous System Tumor Statistics, 2021.” CA: A Cancer Journal for Clinicians 71 (5): 381–406.
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.
Özcan, Hakan, Bülent Gürsel Emiroğlu, Hakan Sabuncuoğlu, Selçuk Özdoğan, Ahmet Soyer, and Tahsin Saygı. 2021. “A Comparative Study for Glioma Classification Using Deep Convolutional Neural Networks.” Mathematical Biosciences and Engineering: MBE 18 (2): 1550–72.
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.
Saba, Tanzila, Ibrahim Abunadi, Tariq Sadad, Amjad Rehman Khan, and Saeed Ali Bahaj. 2021. “Optimizing the Transfer-Learning with Pretrained Deep Convolutional Neural Networks for First Stage Breast Tumor Diagnosis Using Breast Ultrasound Visual Images.” Microscopy Research and Technique, December. https://doi.org/10.1002/jemt.24008.
Tandel, Gopal S., Ashish Tiwari, and O. G. Kakde. 2021. “Performance Optimisation of Deep Learning Models Using Majority Voting Algorithm for Brain Tumour Classification.” Computers in Biology and Medicine 135 (August): 104564.
Tazin, Tahia, Sraboni Sarker, Punit Gupta, Fozayel Ibn Ayaz, Sumaia Islam, Mohammad Monirujjaman Khan, Sami Bourouis, Sahar Ahmed Idris, and Hammam Alshazly. 2021. “A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network.” Computational Intelligence and Neuroscience 2021 (December): 2392395.
Wang, Huiquan, Chunli Liu, Zhe Zhao, Chao Zhang, Xin Wang, Huiyang Li, Haixiao Wu, et al. 2021. “Application of Deep Convolutional Neural Networks for Discriminating Benign, Borderline, and Malignant Serous Ovarian Tumors From Ultrasound Images.” Frontiers in Oncology 11 (December): 770683.
Wang, You-Wei, Chii-Jen Chen, Teh-Chen Wang, Hsu-Cheng Huang, Hsin-Ming Chen, Jin-Yuan Shih, Jin-Shing Chen, Yu-Sen Huang, Yeun-Chung Chang, and Ruey-Feng Chang. 2021. “Multi-Energy Level Fusion for Nodal Metastasis Classification of Primary Lung Tumor on Dual Energy CT Using Deep Learning.” Computers in Biology and Medicine 141 (December): 105185.
Williams, Simon, Hugo Layard Horsfall, Jonathan P. Funnell, John G. Hanrahan, Danyal Z. Khan, William Muirhead, Danail Stoyanov, and Hani J. Marcus. 2021. “Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm.” Cancers. https://doi.org/10.3390/cancers13195010.
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Published
Mar 18, 2022
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Mohan, R. ., Ganapathy, K. ., & Arunmozhi, R. (2022). Brain tumor classification of magnetic resonance Images using novel CNN-based medical image Analysis and Detection network in comparison with AlexNet: Brain tumor classification using novel CNN. Journal of Population Therapeutics and Clinical Pharmacology, 29(01). https://doi.org/10.47750/jptcp.2022.898
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How to Cite
Mohan, R. ., Ganapathy, K. ., & Arunmozhi, R. (2022). Brain tumor classification of magnetic resonance Images using novel CNN-based medical image Analysis and Detection network in comparison with AlexNet: Brain tumor classification using novel CNN. Journal of Population Therapeutics and Clinical Pharmacology, 29(01). https://doi.org/10.47750/jptcp.2022.898