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Research Article Open access CC BY 4.0

MRI-based Brain Tumor Image Classification Using CNN

Sher Shermin Azmiri Khan, Ayesha Aziz Prova, Uzzal Kumar Acharjee

Asian Journal of Research in Computer Science · pp. 1–10 · Published 2 Jan 2023

10.9734/ajrcos/2023/v15i1310

Abstract

Though all brain tumors are not cancerous but they caused a critical disease produced by irrepressible and unusual dividing of cells. For the case of Medical diagnostics of many diseases, the health industry needs help, the current development in the arena of deep learning has assisted to detect diseases. In recent years medical image classification has gained remarkable attention. The most well-known neural network model for image classification problems is the Convolutional Neural Network (CNN). CNN is the frequently employed machine-learning algorithm that is used in Visual learning and Image Recognition research. It is considered to derive features adaptively through convolution, activation, pooling, and fully connected layers. In our paper, we present the convolutional neural network method to determine cancerous and non-cancerous brain tumors. We also used Data Augmentation and Image Processing to classify brain (Magnetic Resonance Imaging (MRI). We used two significant steps in our proposed system. First, different image processing techniques are used to preprocess the images and secondly we classify the preprocessed image using CNN. Brain tumor classification is a process of identifying and separating the cancerous and non-cancerous brain tissues and labeling them automatically. We use the famous machine learning algorithms Convolutional Neural Network which is broadly employed for image classifications. This experiment is conducted on a dataset of 2065 images. In our dataset number of training, examples are 1445, the number of validation examples is 310, and the number of testing example is 310. We also used data augmentation to raise the number of the dataset. We achieved a high testing accuracy of 94.39%. The proposed system displayed sufficient accuracy on the dataset and beat many of the noticeable present methods.

Brain MRI images CNN augmentation tumor deep learning machine learning

References (19)

  1. 1 Rectified Linear Units Improve Restricted Boltzmann Machines
  2. 2 On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning [DOI]
  3. 3 Preparing Medical Imaging Data for Machine Learning [DOI]
  4. 4 Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework [DOI]
  5. 5 A Review on a Deep Learning Perspective in Brain Cancer Classification [DOI]
  6. 6 Brain Tumor Classification Using Convolutional Neural Networks [DOI]
  7. 7 Brain tumor classification in MRI image using convolutional neural network [DOI]
  8. 8 Oxidative stress promotes myofibroblast differentiation and tumour spreading [DOI]
  9. 9 Brain tumor classification from multi-modality MRI using wavelets and machine learning [DOI]
  10. 10 Brain Tumor Classification Using Convolutional Neural Network [DOI]
  11. 11 Brain Tumor Classification Using Deep Learning [DOI]
  12. 12 CNN Based Multiclass Brain Tumor Detection Using Medical Imaging [DOI]
  13. 13 X-ray detectors in medical imaging [DOI]
  14. 14 IoT data visualization for business intelligence in corporate finance [DOI]
  15. 15 Mathematical methods in medical image processing [DOI]
  16. 16 Radiation-reduction strategies in cardiac computed tomographic angiography [DOI]
  17. 17 Performance Evaluation of Image Segmentation using Objective Methods [DOI]
  18. 18 Use of Training, Validation, and Test Sets for Developing Automated Classifiers in Quantitative Ethnography [DOI]
  19. 19 A new deep CNN for brain tumor classification [DOI]

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