A Novel Mask R-CNN based Approach to Brain Tumour Detection
Keywords:
Brain tumour detection, Medical imaging, Image segmentation, Convolutional neural network, Mask R-CNNAbstract
When abnormal cells form in the brain, it is called a brain tumour. The unique blocking and proliferation of abnormal tissues in the brain can be found using magnetic resonance imaging (MRI) imaging of the brain. Due to advancements in medical imaging technology for auto detecting equipment, it is no longer necessary for a doctor to look at MRI scans to determine whether a patient has a tumour. As a result, it has proven handy for patients who do not wish to see a doctor immediately. Our study describes a method for segmenting abnormal brain tissues and determining whether the patient has a tumour. This approach detects a unique area of the brain and forecasts the likelihood of a tumour developing there. Mask regional-based convolutional neural network (Mask R-CNN) is a pre-trained deep neural network model that is used to distinguish objects from an image such as cars, animals, trees, and other objects. In comparison to many other similar methods based on MLP, VGG-16 model, and U-net model, we discovered that Mask R-CNN method performs the best. The clarity of the MRI scans has a big impact on the accuracy. The proposed system was able to outperform similar systems on the same dataset, achieving 74 percent Intersection over Union (IoU) score on the reference dataset, Brain MRI Images for Brain Tumour Detection.
Downloads
References
The Wikipedia page about “Brain tumor”, available: https://en.wikipedia.org/wiki/Brain_tumor
“The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary”, Springer-Verlag Berlin Heidelberg, 9 February 2016, available: http://braintumor.org/wp-content/assets/WHO-Central-Nervous-System-Tumor-Classification.pdf
“Medical imaging”, from wikipedia, available: https://en.wikipedia.org/wiki/Medical_imaging
The Wikipedia page about “Magnetic resonance imaging”, available: https://en.wikipedia.org/wiki/Magnetic_resonance_imaging
Amin, Javaria, Muhammad Sharif, Anandakumar Haldorai, Mussarat Yasmin, and Ramesh Sundar Nayak. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00563-y
Khan MA, Lali IU, Rehman A, Ishaq M, Sharif M, Saba T et al (2019) Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 82:909–922
Agrawal U, Brown EN, Lewis LD (2020) Model-based physiological noise removal in fast fMRI. Neuroimage 205:1–18
Malla, S., M. J. . Meena, O. . Reddy. R, V. . Mahalakshmi, and A. . Balobaid. “A Study on Fish Classification Techniques Using Convolutional Neural Networks on Highly Challenged Underwater Images”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 4, Apr. 2022, pp. 01-09, doi:10.17762/ijritcc.v10i4.5524.
Dubey YK, Mushrif MM (2016) FCM clustering algorithms for segmentation of brain MR images. Adv Fuzzy Syst 2016:8
Irshad M, Muhammad N, Sharif M, Yasmeen M (2018) Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation. Eur Phys J Plus 133:1–14
Rewari R (2021) Automatic tumor segmentation from MRI scans. Stanford University, Stanford
Havaei M, Davy A,Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31
Jiang J, Wu Y, Huang M, Yang W, Chen W, Feng Q (2013) Brain tumor segmentation in multimodal MR images based on learning population-and patient-specific feature sets. Comput Med Imaging Graph 37:512–521
Ortiz A, Gorriz JM, Ramírez J, Salas-Gonzalez D, Alzheimer’s Disease Neuroimaging Initiative (2013) Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization 114:118–131
He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.available: https://arxiv.org/abs/1703.06870
Girshick, Ross. "Fast r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 1440-1448. 2015., available: https://arxiv.org/abs/1504.08083
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems 28 (2015)., available: https://arxiv.org/pdf/1506.01497.pdf
Zhang, L., Lin, L., Liang, X., He, K. (2016). Is Faster R-CNN Doing Well for Pedestrian Detection?. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science(), vol 9906. Springer, Cham. https://doi.org/10.1007/978-3-319-46475-6_28
Huang, Jonathan, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer et al. "Speed/accuracy trade-offs for modern convolutional object detectors." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7310-7311. 2017.
O Pinheiro, Pedro O., Ronan Collobert, and Piotr Dollár. "Learning to segment object candidates." Advances in neural information processing systems 28 (2015).
Dai, Jifeng, Kaiming He, and Jian Sun. "Instance-aware semantic segmentation via multi-task network cascades." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3150-3158. 2016.
Ananthakrishnan, B., V. . Padmaja, S. . Nayagi, and V. . M. “Deep Neural Network Based Anomaly Detection for Real Time Video Surveillance”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 4, Apr. 2022, pp. 54-64, doi:10.17762/ijritcc.v10i4.5534.
Li, Yi, Haozhi Qi, Jifeng Dai, Xiangyang Ji, and Yichen Wei. "Fully convolutional instance-aware semantic segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2359-2367. 2017.
Andrie Dazlee, N. M. A., Abdul Khalil, S., Abdul-Rahman, S., & Mutalib, S. (2022). Object Detection for Autonomous Vehicles with Sensor-based Technology Using YOLO. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 129–134. https://doi.org/10.18201/ijisae.2022.276
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems 28 (2015).
Girshick, R., Donahue, J., Darrell, T. and Malik, J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
“Deep Residual Learning for Image Recognition”, Computer Vision and Pattern Recognition, by KAIMING HE, XIANGYU ZHANG, SHAOQING REN, JIAN SUN, 10 DEC 2015, AVAILABLE: HTTPS://ARXIV.ORG/ABS/1512.03385
“Convolutional neural network”, From Wikipedia, available: https://en.wikipedia.org/wiki/Convolutional_neural_network
Pawan Kumar Tiwari, Mukesh Kumar Yadav, R. K. G. A. . (2022). Design Simulation and Review of Solar PV Power Forecasting Using Computing Techniques. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(5), 18–27. https://doi.org/10.17762/ijrmee.v9i5.370
T. Hossain, F. S. Shishir, M. Ashraf, M. A. Al Nasim and F. Muhammad Shah, "Brain Tumor Detection Using Convolutional Neural Network," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019, pp. 1-6, doi: 10.1109/ICASERT.2019.8934561.
S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1-6, doi: 10.1109/ICEngTechnol.2017.8308186.
R. Ezhilarasi and P. Varalakshmi, "Tumor Detection in the Brain using Faster R-CNN," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, 2018, pp. 388-392, doi: 10.1109/I-SMAC.2018.8653705.
“Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme”, 2010 Dec 1, available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2863141
“Brain Tumor Dataset”, By Navoneel Chakrabarty, Nov 2018, available: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
“TensorFlow”, by Google, 9 November 2015, available: https://www.tensorflow.org/
“Deep Neural Networks”, by Wikipedia, available: https://en.wikipedia.org/wiki/Deep_learning#Deep_neural_networks
“Keras”, by François Chollet, 27 March 2015, available: https://keras.io/
“OpenCV”, by Intel, June 2000, available: https://opencv.org/about/
“COCO dataset”, COCO is a large-scale object detection, segmentation, and captioning dataset, available: https://cocodataset.org/
“Labelme Software”, Image Polygonal Annotation with Python, by Kentaro Wada, available: https://github.com/wkentaro/labelme
“Intersection over Union”, from Wikipedia, available: https://en.wikipedia.org/wiki/Jaccard_index
Shah, P.J. and Shah, T., 2021. Identification of Breast Tumor Using Hybrid Approach of Independent Component Analysis and Deep Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 9(4), pp.209-219.
Altan, G., 2020. Deep Learning-based Mammogram Classification for Breast Cancer. International Journal of Intelligent Systems and Applications in Engineering, 8(4), pp.171-176.
He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.
“Computer Vision: Instance Segmentation with Mask R-CNN”, by Renu Khandelwal, available: https://towardsdatascience.com/computer-vision-instance-segmentation-with-mask-r-cnn-7983502fcad1
“Mask R-CNN”, available: https://developpaper.com/mask-r-cnn/
Ghazaly, N. M. . (2022). Data Catalogue Approaches, Implementation and Adoption: A Study of Purpose of Data Catalogue. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 01–04. https://doi.org/10.17762/ijfrcsce.v8i1.2063
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
