Brain Tumor Detection & Segmentation using Deep Learning

Daksh Ailawadi*, Nipun Agarwal**, Parth Agarwal***, Manish Rana****
*-**** Department of Computer Science, Thakur College of Engineering and Technology, Mumbai, India.
Periodicity:June - August'2022


The goal of this work is to identify brain tumors and improve care for those who are suffering. Tumors are the term used to denote abnormal cell growth in the brain, while cancer is the term used to describe malignant tumors. Brain cancer regions are typically discovered via Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans. For the detection of brain tumors, further methods include molecular testing, lumbar puncture, cerebral angiogram, and positron emission tomography. Images from an MRI scan are used in this study to analyze the disease stage. The goals of this research are to segment the tumor region and to identify the abnormal image. The segmented mask can be used to evaluate the tumor's density, which will aid in treatment. ResNet algorithm is used to analyze MRI pictures and find anomalies.


Image Segmentation, Brain Tumor, MRI, ResNet, CNN.

How to Cite this Article?

Ailawadi, D., Agarwal, N., Agarwal, P., and Rana, M. (2022). Brain Tumor Detection & Segmentation using Deep Learning. i-manager’s Journal on Computer Science, 10(2), 27-33.


[1]. Akhila, J. A., Markose, C., & Aneesh, R. P. (2017, July). Feature extraction and classification of dementia with neural network. In 2017, International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (pp. 1446-1450). IEEE.
[2]. Chakrabarty, N. (2019). Brain mri images for brain tumor detection. Journal of Experimental Medicine (JEM), 216, 539-555.
[3]. Chen, W., Qiao, X., Liu, B., Qi, X., Wang, R., & Wang, X. (2017, October). Automatic brain tumor segmentation based on features of separated local square. In 2017 Chinese Automation Congress (CAC) (pp. 6489-6493). IEEE.
[4]. Damodharan, S., & Raghavan, D. (2015). Combining tissue segmentation and neural network for brain tumor detection. International Arab Journal of Information Technology (IAJIT), 12(1), 42-52.
[5]. Gurbină, M., Lascu, M., & Lascu, D. (2019, July). Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines. In 2019, 42nd International Conference on Telecommunications and Signal Processing (TSP) (pp. 505-508). IEEE.
[6]. Hemanth, G., Janardhan, M., & Sujihelen, L. (2019, April). Design and implementing brain tumor detection using machine learning approach. In 2019, 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1289-1294). IEEE.
[7]. Louis, D. N., Perry, A., Reifenberger, G., Von Deimling, A., Figarella-Branger, D., Cavenee, W. K., ... & Ellison, D. W. (2016). The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica, 131(6), 803-820.
[8]. Mathew, A. R., & Anto, P. B. (2017, July). Tumor detection and classification of MRI brain image using wavelet transform and SVM. In 2017 International Conference on Signal Processing and Communication (ICSPC) (pp. 75-78). IEEE.
[9]. Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017, September). Tversky loss function for image segmentation using 3D fully convolutional deep networks. In International Workshop on Machine Learning in Medical Imaging (pp. 379-387). Springer, Cham.
[10]. Somasundaram, S., & Gobinath, R. (2019). Early Brain Tumour Prediction using an Enhancement Feature Extraction Technique and Deep Neural Networks. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(10).
[11]. Soumya, R. S., Neethu, S., Niju, T. S., Renjini, A., & Aneesh, R. P. (2016, July). Advanced earlier melanoma detection algorithm using colour correlogram. In 2016 International Conference on Communication Systems and Networks (ComNet) (pp. 190-194). IEEE.
[12]. Suresh, M., Sinha, A., & Aneesh, R. P. (2019). Real- Time Hand Gesture Recognition Using Deep Learning. International Journal of Innovations and Implementations in Engineering, 1, 11-15.
[13]. Thanveersha, M., Jayni, J., Fathima, T., & Sinha, A. (2019). Automatic brain hemorrhage detection using artificial neural network. International Journal of Innovations and Implementations in Engineering.
[14]. Zhang, Z., Liu, Q., & Wang, Y. (2018). Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters, 15(5), 749-753.
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