Multi-Class Classification of Brain Disease using Machine Learning-Deep Learning approaches and Ranking based Similar Image Retrieval from Large Dataset

Authors

  • Dhanraj R. Dhotre Associate Professor, Department of Computer Science and Engineering, MIT School of Computing, MIT Art Design and Technology University, Loni, Pune, India
  • Ranjana Dahake Assistant Professor, Department of Computer Engineering, MET’s Institute of Engineering,Nashik, India
  • Nitin Choubey Faculty, SVKM'S NMIMS Mukesh Patel School of Technology Management & Engineering, Shirpur, Dhule
  • Anand Khandare Associate Professor, Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai
  • Megharani Patil Associate Professor, Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai
  • Ashvini Jadhav Assistant Professor, Department of information Technology, MIT School of Computing, MIT Art Design and Technology University, Loni, Pune, India

Keywords:

Python, Data Augmentation, Deep Learning, Convolutional Neural Network, Transfer Learning, Support Vector Machine, Magnetic Resonance Imagining

Abstract

A brain tumor is a very serious and life-threatening disease that requires immediate medical attention. Idetification and determination of an appropriate tumor type is the most important and challenging aspect of treating a patient with a brain tumor. This step takes considerable time and multiple tests are required for accurate identification. It is crucial to design the best treatment plan as soon as possible to improve the patient's chances of living a long and healthy life because brain tumors can have long-lasting and devastating effects on a patient's physical, mental, and emotional well-being. Using advanced technology, the proposed solution aims to identify the type of brain tumor quickly and accurately, which can save valuable time for doctors to provide additional treatments and ultimately save patients' lives. The dataset used in the study consists of 7023 T1-weighted contrast-enhanced images that have been cleaned and enhanced, and the proposed method encompasses the use of various deep learning models based on various kinds of Neural Networks (CNN, TL) and classifiers (SVM) to classify brain tumors into Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor i.e., CNN got an accuracy of 95% where as for TL  i.e., MobileNetV2 gives an accuracy of 90% and SVM 90%. Apart from proposed work comparison of different TL models  is done that can be suitable for developing a full robust application for Brain Tumor Detection and Classification.   

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References

Kadam, Ankita, Sartaj Bhuvaji, and Sujit Deshpande. "Brain Tumor Classification using Deep Learning Algorithms."

Chaganti, Sai Yeshwanth, et al. "Image Classification using SVM and CNN." 2020 International conference on computer science, engineering and applications (ICCSEA). IEEE, 2020.

Baranwal, Shubham Kumar, et al. "Performance analysis of brain tumour image classification using CNN and SVM." 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2020.

Sultan, Hossam H., Nancy M. Salem, and Walid Al-Atabany. "Multi-classification of brain tumor images using deep neural network." IEEE access 7 (2019): 69215-69225.

Rahmani, Mohammad Khalid Imam, et al. "A Content-Based Medical Image Retrieval Algorithm." 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). IEEE, 2022.

Devareddi, Ravi Babu, and A. Srikrishna. "Review on Content-based Image Retrieval Models for Efficient Feature Extraction for Data Analysis." 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022.

Alqasemi, Fahd A., et al. "Feature selection approach using KNN supervised learning for content-based image retrieval." 2019 First International Conference of Intelligent Computing and Engineering (ICOICE). IEEE, 2019.

Liu, Zihao, et al. "Commodity Image Retrieval Method Based on CNN and Score Fusion." 2021 International Conference on Culture-oriented Science & Technology (ICCST). IEEE, 2021.

Kaur, Palwinder, and Rajesh Kumar Singh. "An efficient analysis of machine learning algorithms in CBIR." 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE, 2020.

Likhitha, Tata Lakshmi Durga, et al. "A Detailed Review on CBIR and Its Importance in Current Era." 2021 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2021.

Rian, Zakhayu, Viny Christanti, and Janson Hendryli. "Content-based image retrieval using convolutional neural networks." 2019 IEEE International Conference on Signals and Systems (ICSigSys). IEEE, 2019.

Kannagi, A., and Ravikumar Lanke. "Image Retrieval based on Deep Learning-Convolutional Neural Networks." 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC). IEEE, 2022.

Batra, Anjali, and Meenakshi Sharma. "Analysis of distance measures in content based image retrieval." Global Journal of Computer Science and Technology 14.G2 (2014): 11-16.

Hanif, Md Abu, et al. "Role of CBIR In a Different fields-An Empirical Review." 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST). IEEE, 2022.

Madugunki, Meenakshi, et al. "Comparison of different CBIR techniques." 2011 3rd International Conference on Electronics Computer Technology. Vol. 4. IEEE, 2011.

Hameed, Ibtihaal M., Sadiq H. Abdulhussain, and Basheera M. Mahmmod. "Content-based image retrieval: A review of recent trends." Cogent Engineering 8.1 (2021): 1927469.

Varma, Nehal M., and Anamika Choudhary. "Evaluation Of Distance Measures In Content Based Image Retrieval." 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2019.

Mohamed, Ouhda, et al. "Content-based image retrieval using convolutional neural networks." Lecture Notes in Real-Time Intelligent Systems. Springer International Publishing, 2019.

Bhat , A. H. ., & H V, B. A. . (2023). E2BNAR: Energy Efficient Backup Node Assisted Routing for Wireless Sensor Networks . International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 193–204. https://doi.org/10.17762/ijritcc.v11i3s.6181

Matti Virtanen, Jan de Vries, Thomas Müller, Daniel Müller, Giovanni Rossi. Machine Learning for Intelligent Feedback Generation in Online Courses . Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/188

Agrawal, S. A., Umbarkar, A. M., Sherie, N. P., Dharme, A. M., & Dhabliya, D. (2021). Statistical study of mechanical properties for corn fiber with reinforced of polypropylene fiber matrix composite. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1072

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Published

03.09.2023

How to Cite

Dhotre, D. R. ., Dahake , R. ., Choubey, N. ., Khandare, A. ., Patil, M. ., & Jadhav, A. . (2023). Multi-Class Classification of Brain Disease using Machine Learning-Deep Learning approaches and Ranking based Similar Image Retrieval from Large Dataset . International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 771–782. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3550

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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