Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Classification of Brain Tumours in MRI Images using a Convolutional Neural Network

Author(s): Isha Gupta, Swati Singh, Sheifali Gupta and Soumya Ranjan Nayak*

Volume 20, 2024

Published on: 07 July, 2023

Article ID: e270323214998 Pages: 9

DOI: 10.2174/1573405620666230327124902

open_access

Abstract

Introduction: Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning and image identification is CNN.

Methods: In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16, ResNet-50, and Inceptionv3 using the technique which is transfer learning.

Results: Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model accuracy achieved is 94%, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90%, whereas ResNet- 50 reached 86% and Inception v3 obtained 64% accuracy.

Conclusion: When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly higher accuracy outcomes and a reduction in losses.

Keywords: Transfer learning, Brain tumour, Deep learning, Medical imaging, Classification, National brain tumor.

[1]
NBTS, National brain tumor society: Quick brain tumour facts. 2020. Available from: https://braintumor.org/brain-tumor- information/brain-tumor-facts/
[2]
Cancer. Net, Brain tumor: Statistics. 2020. Available from:https://www.cancer.net/cancertypes/brain-tumor/statistics
[3]
NHS, National health service: Brain tumours. 2020. Available fromhttps://www.nhs.uk/conditions/brain-tumours
[4]
Basheera S, Ram MSS. Classification of brain tumours using deep features extracted using CNN. J Phys 2019; 1172: 012016.
[5]
Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 2019; 30: 174-82.
[http://dx.doi.org/10.1016/j.jocs.2018.12.003]
[6]
Carlo R, Renato C, Giuseppe C, Lorenzo U, Giovanni I, Domenico S. Distinguishing functional from non-functional pituitary macroadenomas with a machine learning analysis. Mediterranean Conference on Medical and Biological Engineering and Computing. 1822-9.
[7]
Khawaldeh S, Pervaiz U, Rafiq A, Alkhawaldeh R. Noninvasive grading of gliomatumourr using magnetic resonance imaging with convolutional neural networks. Appl Sci 2017; 8(1): 27.
[http://dx.doi.org/10.3390/app8010027]
[8]
Abiwinanda N, Hanif M, Hesaputra S, Handayani A, Mengko TR. Brain tumouror classification using convolutional neural network. World Congress on Medical Physics and Biomedical Engineering.
[9]
Das S, Aranya R, Labiba N. Brain tumourmor classification using convolutional neural network. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT).
[10]
Romeo V, Cuocolo R, Ricciardi C, et al. Predictiontumourumor grade and nodal status in oropharyngeal and oral casquamous cell-cell carcinoma using a radiomic approach. Anticancer Res 2020; 40(1): 271-80.
[http://dx.doi.org/10.21873/anticanres.13949] [PMID: 31892576]
[11]
Romeo V, Cuocolo R, Ricciardi C, Ugga L, Cocozza S, Verde F. Prediction of tumor grade and nodal status in oropharyngeal & oral cavity squamous-cell carcinoma using a radiomic approach. Anticancer Res 2020; 40: 271-81.
[12]
Talo M, Baloglu UB, Yıldırım Ö, Rajendra Acharya U. Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res 2019; 54: 176-88.
[http://dx.doi.org/10.1016/j.cogsys.2018.12.007]
[13]
Rehman A, Naz S, Razzak MI, Akram F, Imran M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst Signal Process 2020; 39(2): 757-75.
[http://dx.doi.org/10.1007/s00034-019-01246-3]
[14]
Çinar A, Yildirim M. Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med Hypotheses 2020; 139: 109684.
[http://dx.doi.org/10.1016/j.mehy.2020.109684] [PMID: 32240877]
[15]
Chakrabarty N. Brain MRI images dataset for tumourtumour detection, Kaggle. 2019. Available from: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
[16]
Canny JF. Canny edge detection, open source computer vision, openCV. Available from: https://docs.opencv.org/trunk/da/d22/tutorial_py_canny.html
[17]
Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data 2019; 6(1): 60.
[http://dx.doi.org/10.1186/s40537-019-0197-0]
[18]
Keras, Image data preprocessingkeras API, Keras documentation. Available from: https://keras.io/api/preprocessing/image
[19]
Nair V, Hinton G. Rectified Linear Units Improve Restricted Boltzmann Machines. ICML 2010; 807-14.
[20]
Mannor S, Peleg D. Rubinsteicross-entropyentropy method for classification. Proceedings of the 22nd international conference on Machine learning.
[http://dx.doi.org/10.1145/1102351.1102422]
[21]
a) Kingma DP, Ba J. A method for stochastic optimization. arXiv: 14126980 2014.;
b) Robbins H, Munro S. A stochastic approximation method. Ann Math Stat 1951; 22(3): 400-7.
[22]
Hinton G. Neural networks for machine learning online course lecture 6a, Coursera. Available from: http://www.cs.toronto.edu/tijmen/csc321/slides/lecture_slides_lec6.pdf
[23]
Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2010; 22(10): 1345-59.
[http://dx.doi.org/10.1109/TKDE.2009.191]
[24]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv: 14091556 2015.
[25]
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition.
[26]
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition.
[http://dx.doi.org/10.1109/CVPR.2016.308]
[27]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition.

© 2024 Bentham Science Publishers | Privacy Policy