Brain Tumor Detection Through Image Processing and Machine Learning Techniques


  • Srinivas Kumar Palvadi Research Scholar, Department of Computer Science and Engineering, Pondicherry University, Puducherry, Kalapet - 605014
  • K. Suresh Joseph Associate Professor, Department of Computer Science and Engineering, Pondicherry University, Puducherry, Kalapet - 605014


Brain Tumor, Machine Learning Techniques, Image Processing


Mind was an administrative unit in human body. It controls the capabilities, for example, memory, vision, hearing, information, character, critical thinking, and so on. Presently a day's growth is second driving reason for disease. Because of malignant growth huge no of patients are in harm's way. The clinical field needs quick, robotized, productive and dependable strategy to recognize growth like cerebrum cancer. Discovery assumes vital part in treatment. In the event that legitimate recognition of growth is potential, specialists keep a patient out of risk. Different picture handling procedures are utilized in this application. Utilizing this application specialists give legitimate treatment and save various growth patients. A growth is only overabundance cells filling in an uncontrolled way. Cerebrum cancer cells fill such that they in the long run take up every one of the supplements implied for the sound cells and tissues, which brings about mind disappointment. At present, specialists find the position and the area of cerebrum growth by taking a gander at the MR Pictures of the mind of the patient physically. This outcomes in off base location of the cancer and is considered very tedious.
Mechanized imperfection recognition in clinical imaging has turned into the new field in a few clinical demonstrative applications. Computerized discovery of growth in X-ray is exceptionally critical as it gives data about strange tissues which is important for arranging treatment. The regular technique for deformity location in attractive reverberation cerebrum pictures is human examination. This technique is unfeasible because of enormous measure of information. Thus, trusted and programmed arrangement plans are fundamental to forestall the demise pace of human. Thus, mechanized cancer recognition techniques are created as it would save radiologist time and get a tried exactness. The X-ray cerebrum growth recognition is convoluted assignment because of intricacy and change of cancers. In this venture, we propose the AI calculations to defeat the downsides of customary classifiers where cancer is identified in mind X-ray utilizing AI calculations. AI and picture classifier can be utilized to recognize disease cells in mind through X-ray effectively.


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Methil, Aryan. (2021). Brain Tumor Detection using Deep Learning and Image Processing. 100-108. 10.1109/ICAIS50930.2021.9395823.

Amin, J., Sharif, M., Haldorai, A. et al. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell. Syst. (2021).

Gurunathan, A., Krishnan, B. A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor. Brain Imaging and Behavior (2022).

Brain Tumor Detection using Convolutional Neural Network By Poornimasre Jegannathan in Turkish Journal of Computer and Mathematics Education Vol.12 No.11 (2021), 686-692.

Yadav, S.S., Jadhav, S.M. Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data 6, 113 (2019).

Ranjbarzadeh, R., Bagherian Kasgari, A., Jafarzadeh Ghoushchi, S. et al. Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 11, 10930 (2021).

N Saranya et al 2021 J. Phys.: Conf. Ser. 1916 012206.

Seetha J, Raja S. S. Brain Tumor Classification Using Convolutional Neural Networks. Biomed Pharmacol J 2018;11(3).

Arabahmadi M, Farahbakhsh R, Rezazadeh J. Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging. Sensors. 2022; 22(5):1960.

Arkapravo Chattopadhyay, Mausumi Maitra,MRI-based brain tumour image detection using CNN based deep learning method,Neuroscience Informatics,Volume 2, Issue 4,2022.

Díaz-Pernas, F.J.; Martínez-Zarzuela, M.; Antón-Rodríguez, M.; González-Ortega, D. A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network. Healthcare 2021, 9, 153.

Brain Tumor Classification in MRI Images Using En-CNN in international journal of intelligent engineering & systems Vol.14, No.4, 2021.

Omar Adil Kamil, Shaymaa W. Al-Shammari, IoT Framework for Brain Tumor Classification Using Optimized CNN-MRFO Model, American Journal of Bioinformatics Research, Vol. 11 No. 1, 2021, pp. 32-37. doi: 10.5923/j.bioinformatics.20211101.02.

A Survey On Brain Tumor Detection Based On Structural MRI Using Machine Learning And Deep Learning Techniques. Dhanashri Joshi, Hemlata Channe INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 04, APRIL 2020.

DETECTION OF BRAIN TUMOR USING TRANSFER LEARNING Apoorva Ramaiah , Hemalatha J , Himanshu Raj, Samarth Mohapatra, Prof. Kavitha K VOLUME-8, ISSUE-7, 2021.

PREDICTIVE MODELLING OF BRAIN TUMOR DETECTION USING DEEP LEARNING Suraj Patil , Dr D. K. Kirange , Varsha Nemade journal of critical reviews, VOL 7, ISSUE 04, 2020.

Brain Tumor Detection and Classification Using Convolutional Neural Networks Darshan Bhamare, Vinay Gupta, Vijay Sawale, Ajay Ghosade International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 8, Issue 11, November 2021.

Bathe, Kavita and Rana, Varun and Singh, Sanjay and Singh, Vijay, Brain Tumor Detection Using Deep Learning Techniques (MAY 7, 2021). Proceedings of the 4th International Conference on Advances in Science & Technology (ICAST2021).

Sharma, Kirti,et al. "Study on Brain Tumor Classification Through MRI Images Using a Deep Convolutional Neural Network." IJIRR vol.12, no.1 2022: pp.1-19.

Brain tumor detection from MRI images with using proposed deep learning model: the partial correlation-based channel selection Atınç YILMAZ et al.

Glioma Tumor Detection Through Faster Region-Based Convolutional Neural Networks Using Transfer Learning Shrwan Ram, anil Gupta Volume 07, Issue 02, 2020.




How to Cite

Palvadi, S. K. ., & Joseph, K. S. . (2024). Brain Tumor Detection Through Image Processing and Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 382 –. Retrieved from



Research Article