Detection and Classification of Brain Tumor and Breast Cancer by Using an Efficient Method Based on Image Processing and Fuzzy Inference System
Brain tumor and breast cancer are considered to be the most fatal cases for the health of people in modern days. Therefore, early and precise detection of these cases can save many lives all over the world. However, detection and classification of the tumor/cancer area precisely can help the doctors for diagnosing and treatment. In this article, a method based on different noise removal and image adjustment techniques integrated with a Mamdani Fuzzy Inference System (FIS) is proposed to efficiently detect and classify both brain tumor and breast cancer. Firstly, this method is used for detection and classification of brain tumor from the standard Magnetic Resonance Imaging (MRI) dataset. Then, the same method is utilized to detect and classify breast cancer from standard Mammography image dataset with a little modification of the inputs and outputs of the FIS. The use of Otsu’s Method for Global Image Thresholding of the intensity adjusted image and the Robert’s Method for edge detection of the cancer/tumor area increase the efficiency of the method. Furthermore, the performance of this technique is compared with the other conventional neural network and fuzzy based techniques. Accordingly, it is found that this method is competitive with them on various performance parameters.
Amin Kabir Anaraki, Moosa Ayati, Foad Kazemi, “Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithm,” Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences, vol.39, no. 1, pp. 63-74, 2019.
Tonmoy Hossain, Fairuz Shadmani Shishir , Mohsena Ashraf, “Brain Tumor Detection Using Convolutional Neural Network,”in Proc.1st International Conference on Advances in Science, Engineering and Robotics Technology, 2019.
G. Florimbi et al., "Towards Real-Time Computing of Intraoperative Hyperspectral Imaging for Brain Cancer Detection Using Multi-GPU Platforms," in IEEE Access, vol. 8, pp. 8485-8501, 2020, doi: 10.1109/ACCESS.2020.2963939.
Seetha J, Raja S. S., “Brain Tumor Classification Using Convolutional Neural Networks,” in Biomedical and Pharmacology Journal, vol. 11, no.3, pp. 1457-1461, 2018.
M. A. Bakr Siddique, S. Sakib, M. M. Rahman Khan, A. K. Tanzeem, M. Chowdhury and N. Yasmin, "Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images," in Proc. Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020, pp. 909-914, doi: 10.1109/I-SMAC49090.2020.9243461.
Nilesh Bhaskarrao Bahadure, Arun Kumar Ray, Har Pal Thethi, "Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM,” in International Journal of Biomedical Imaging, vol. 2017, Article ID 9749108, 2017, Available: https://doi.org/10.1155/2017/9749108.
Badža, Milica M., and Marko Č. Barjaktarović, "Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network," in Applied Sciences, vol. 10, no. 6, 2020, Available: https://doi.org/10.3390/app10061999.
T. Chithambaram, K. Perumal, “Brain Tumor Detection and Segmentation in MRI Images
Using Neural Network,” in International Journal of Advanced Research in Computer Science and Software Engg., vol.7, no. 3, pp. 155-164, 2017.
Harsimranjot Kaur, Reecha Sharma, “Segmentation of Tumor Region from Brain Mri Images Using Fuzzy C-Means Clustering And Seeded Region Growing,” in IOSR Journal of Computer Engineering, Vol. 18, no. 5, pp. 20-24, 2016.
Neha Mathur, Yogesh Kumar Meena, Shruti Mathur and Divya Mathur, “Detection of Brain Tumor in MRI Image through Fuzzy-Based Approach,” in High-Resolution Neuroimaging -Basic Physical Principles and Clinical Applications,Ahmet Mesrur Halefoğlu, IntechOpen, 2018, DOI: 10.5772/intechopen.71485. Available: https://www.intechopen.com/chapters/59087.
Yousif Ahmed Hamad, Konstantin Vasilievich Simonov, and Mohammad B. Naeem, “ Detection of Brain Tumor in MRI Images, Using a Combination of Fuzzy C-Means and Thresholding,” in Int. J. Adv. Pervasive Ubiquitous Comput., vol.11, no.1, pp. 45–60, 2019, DOI:https://doi.org/10.4018/IJAPUC.2019010104.
P Gokila Brindha, M Kavinraj, P Manivasakam and P Prasanth, “Brain tumor detection from MRI images using deep learning techniques,” in Proc. IOP Conf. Series: Materials Science and Engineering 1055 (2021) 012115, doi:10.1088/1757-899X/1055/1/012115.
Reda Shbib, Hussein Trabulsi , Hala Sabagh, “MRI Brain Image Segmentation using Modified Fuzzy Logic Clustering (MFLC),” in International Journal of Engineering Research & Technology, vol. 8, no. 06, pp. 658-666, 2019.
Emmanuel, Lawrence & David, Michael & Adejo, Achonu & Aliyu, Salihu, “Breast Cancer: Tumor Detection in Mammogram Images Using Modified AlexNet Deep Convolution Neural Network,” in Proc. 2020 International Conference in Mathematics, Computer Engineering and Computer Science, IEEE Access, DOI: 10.1109/ICMCECS47690.2020.240870.
Rhaylander Mendes de Miranda Almeida , Dehua Chen, Agnaldo Lopes da Silva Filho
and Wladmir Cardoso Brandao, “Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study,” In Proc. of the 23rd International Conference on Enterprise Information Systems (ICEIS), vol. 1, pp. 660-667, 2021, DOI: 10.5220/0010440906600667.
Saad Awadh Alanazi, M. M. Kamruzzaman, Md Nazirul Islam Sarker, Madallah Alruwaili, Yousef Alhwaiti, Nasser Alshammari, Muhammad Hameed Siddiqi, "Boosting Breast Cancer Detection Using Convolutional Neural Network", in Journal of Healthcare Engineering, vol. 2021, Article ID 5528622, 2021, Available: https://doi.org/10.1155/2021/5528622.
Jose Manuel Ortiz-Rodriguez, Carlos Guerrero-Mendez, Maria del Rosario Martinez Blanco, Salvador Castro-Tapia, Mireya Moreno- Lucio, Ramon Jaramillo-Martinez, Luis Octavio Solis-Sanchez, Margarita de la Luz Martinez-Fierro, Idalia Garza-Veloz, Jose Cruz Moreira Galvan and Jorge Alberto Barrios Garcia, “Breast Cancer Detection by Means of Artificial Neural Networks, Advanced Applications for Artificial Neural Networks,” in Adel El-Shahat, IntechOpen, 2017, DOI: 10.5772/intechopen.71256, Available: https://www.intechopen.com/chapters/57365.
Ali, Shaker & Mutlag, Wamidh, “Early detection for breast cancer by using fuzzy logic,” in Journal of Theoretical and Applied Information Technology, vol. 96, no. 17, pp. 5717-5728, 2018.
M Velmurugan, K Thangavel, R Subash Chandra Boss, “Mammogram Classification using Fuzzy Neural Network,” in International Journal of Computational Intelligence and Informatics, vol. 3, no. 3, pp. 195-206, 2013.
Ragab DA, Sharkas M, Marshall S, Ren J, “Breast cancer detection using deep convolutional neural networks and support vector machines,” in PeerJ 7:e6201, 2019, Available: https://doi.org/10.7717/peerj.6201.
Chowdhary, Chiranji L., Mohit Mittal, Kumaresan P., P. A. Pattanaik, and Zbigniew Marszalek, "An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm" in Sensors, 2020, vol. 20, no. 14: 3903, Available: https://doi.org/10.3390/s20143903.
Sun, Lilei, Huijie Sun, Junqian Wang, Shuai Wu, Yong Zhao, and Yong Xu, "Breast Mass Detection in Mammography Based on Image Template Matching and CNN," in Sensors, 2021, vol. 21, no. 8: 2855, Available: https://doi.org/10.3390/s21082855.
O. O. Soliman, N. H. Sweilam and D. M. Shawky, "Automatic Breast Cancer Detection Using Digital Thermal Images," in 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), 2018, pp. 110-113, doi: 10.1109/CIBEC.2018.8641807.
Parker, James R., “Algorithms for Image Processing and Computer Vision,” in New York, John Wiley & Sons, Inc., Chapter-2, 1997, pp. 23-29.
Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," in IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, 1979, pp. 62-66.
Test data available: https://github.com/ferasbg/glioAI.
Database available: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427.
Dataset available: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection.
Test data available: https://github.com/st186/Detection-of-Breast-Cancer-using-Neural-Networks.
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