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Stream/Area:
-
Subject:
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Paper ID:
IJSREM67588
Volume:
10 |
9 |
Sep 2026
Pages:
1-17
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and engineering
VTU CPGS Kalaburagi.
Dept. Of Computer Science and engineering (MCA
VTU CPGS Kalaburagi.
The motivation behind this study is to detect brain tumour and provide better treatment for the sufferings. The abnormal growths of cells in the brain are called tumours and cancer is a term used to represent malignant tumours. Usually CT or MRI scans are used for the detection of cancer regions in the brain. Positron Emission Tomography, Cerebral Arteriogram, Lumbar Puncture, Molecular testing are also used for brain tumour detection. In this study, MRI scan images are taken to analyse the disease condition. Objective this research works are i) identify the abnormal image ii) segment tumour region. Density of the tumour can be estimated from the segmented mask and it will help in therapy. Deep learning technique is employed to detect abnormality from MRI images. Multi level thresholding is applied to segment the tumour region. Number of malignant pixels gives the density of the affected region.
KEYWORDS
This paper provides a new method for detecting brain tumour by deep learning method. The early detection of cancer helps timely and effective treatment. Kaggle dataset contains good quality of MRI images for research purposes. Different segmentation algorithms were experimented. From this, multilevel thresholding and OTSU thresholding are the best methods for the dataset. Convolutional Neural Network with modified approach helped to get a result with accuracy 98%. Density estimation method is also proposed using Gaussian kernel distribution. This system can be improved to support with a web interface. Detection of different diseases can be also identified from the MRI images. Apart from the density some other parameters can also estimated for therapeutic purposes.
[1] David N. Louis, Arie Perry, et al. , “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary” , Acta Neuropathol , Springer may 2016 [2] Pär Salander, A Tommy Bergenheim, Katarina Hamberg, Roger Henriksson, Pathways from symptoms to medical care: a descriptive study of symptom development and obstacles to early diagnosis in brain tumour patients, Family Practice, Volume 16, Issue 2, April 1999, Pages 143–148 [3] McKinney PA ,”Brain tumours: incidence, survival, and aetiology”,Journal of Neurology, Neurosurgery & Psychiatry 2004;75:ii12-ii17. [4] Heimans, J., Taphoorn, M. Impact of brain tumour treatment on quality of life. J Neurol 249, 955–960 (2002) [5] Malavika Suresh, et al. “Real-Time Hand Gesture Recognition Using Deep Learning”, International Journal of Innovations and Implementations in Engineering(ISSN 2454- 3489), 2019, vol 1 [6] M. Gurbină, M. Lascu and D. Lascu, “Tumor Detection and Classification of MRI Brain Image using Different Wavelet Transforms and Support Vector Machines”, 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary, 2019
Publication Date
9/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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