# Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{8}

^{9}

^{10}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Proposed Method

#### 3.1. Pre-Processing

#### 3.2. Segmentation

#### 3.3. Feature Extraction

#### 3.3.1. Discrete Wavelet Transform (DWT)

^{b}and $a$ > 0) to deliver the DWT, which can be presented as follows:

#### 3.3.2. Log-Polar Transformation (LPT)

^{p}cosθ

^{p}sinθ

#### 3.3.3. Independent Component Analysis (ICA)

#### 3.4. Classification

#### Convolutional Neural Network (CNN)

## 4. Experiment

#### 4.1. Dataset Formation

#### 4.2. Design Simulation

#### 4.3. Simulation and Observation/Output of Simulation

## 5. Result Analysis

#### 5.1. Result 1: Experiment with Distorted/Simulated MRI Image Dataset

- (a)
- Abnormality Classification for Distorted/Simulated MRI Image:

- (b)
- Tumor Classification for Distorted/Simulated MRI Image

#### 5.2. Result 2: Experiment with T-1 Weighted MRI Image Dataset

- (a)
- Abnormality Classification for T-1 Weighted Images

- (b)
**T-1 Weighted Image Tumor Classification**

#### 5.3. Result 3: Experiment with T-2 Weighted MRI Image Dataset

- (a)
- Classification of Abnormalities in T-2 Weighted Images

- (b)
- Tumor Classification for T-2 Weighted Images

**Figure 15.**The accuracy percentages for the T-2 weighted images: (

**a**) RBF; (

**b**) Linear; (

**c**) Polynomial; (

**d**) Quadratic.

## 6. Conclusions

## 7. Limitations

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Zhang, Y.; Wu, L. An MR Brain Images Classifier VIA Principal Component Analysis and Kernel Support Vector Machine. Prog. Electromagn. Res.
**2012**, 130, 369–388. [Google Scholar] [CrossRef] - Jibon, F.A.; Islam, M.S.; Islam, R. Log-polar Transformation based Feature Extraction Method for Tumor Detection and Classification of brain MRI. DUET J.
**2019**, 5, 9–16. [Google Scholar] - Sarhan, A.M. Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network. J. Adv. Med. Med. Res.
**2020**, 32, 15–26. [Google Scholar] [CrossRef] - Kharat, K.D.; Pawar, V.J.; Pardeshi, S.R. Feature Extraction and selection from MRI Images for the brain tumor classification. In Proceedings of the 2016 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 21–22 October 2016. [Google Scholar]
- Fayaz, M.; Torokeldiev, N.; Turdumamatov, S.; Qureshi, M.S.; Qureshi, M.B.; Gwak, J. An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network. Sensors
**2021**, 22, 7480. [Google Scholar] [CrossRef] [PubMed] - Suganaya, S.; Padmaja, S.; Suseendran, G. MRI geometric distortion for brain tumor detection and segmentation. J. Adv. Res. Dyn. Control. Syst.
**2017**, 9, 77–82. [Google Scholar] - Gurusamy, R.; Subramaniam, V. A Machine Learning Approach for MRI Brain Tumor Classification. Comput. Mater. Contin.
**2017**, 53, 91–109. [Google Scholar] - Bauer, S.; Wiest, R.; Nolte, L.-P.; Reyes, M. A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol.
**2013**, 58, 1–44. [Google Scholar] [CrossRef] - Anithadevi, D.; Perumal, K. A Hybrid Approach Based Segmentation. Signal Image Processing Int. J. (SIPIJ)
**2016**, 7, 21–30. [Google Scholar] - Anjana, E.; Kaur, E.R. Review of Image Segmentation Technique. Int. J. Adv. Res. Comput. Sci.
**2017**, 8, 36–39. [Google Scholar] - Zhang, Y.; Dong, Z.; Wang, S.; Ji, G.; Yang, J. Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM). Entropy
**2015**, 17, 1795–1813. [Google Scholar] [CrossRef] - Das, S.; Aranya, O.R.R.; Labiba, N.N. Brain Tumor Classification Using Convolutional Neural Network. In Proceedings of the 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 3–5 May 2019; pp. 1–5. [Google Scholar]
- Joseph, R.P.; Senthil, C.; Manikandan, M. Brain Tumor MRI Image Segmentation and Detection in Image Processing. Int. J. Res. Eng. Technol.
**2014**, 3, 1–5. [Google Scholar] - Torti, E.; Florimbi, G.; Castelli, F.; Ortega, S.; Fabelo, H.; Callicó, G.M.; Marrero-Martin, M.; Leporati, F. Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images. Electronics
**2018**, 7, 283. [Google Scholar] [CrossRef] - Dhanalakshmi, P.; Kanimozhi, T. Automatic Segmentation of Brain Tumor using K-Means Clustering and its Area Calculation. Int. J. Adv. Electr. Electron. Eng. (IJAEEE)
**2013**, 2, 130–134. [Google Scholar] - Hagos, Y.B.; Minh, V.H.; Khawaldeh, S.; Pervaiz, U.; Aleef, T.A. Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering. Methods Protoc.
**2018**, 1, 7. [Google Scholar] [CrossRef] - Minajagi, P.B.; Goudar, R.H. Segmentation of Brain MRI Images using Fuzzy C- Means and DWT. Int. J. Sci. Technol. Eng. (IJSTE)
**2016**, 2, 370–378. [Google Scholar] - Deshmukh, P.; Malge, P.S. Classification of Brain MRI using Wavelet Decomposition and SVM. Int. J. Comput. Appl. (IJCA)
**2016**, 154, 29–33. [Google Scholar] [CrossRef] - Wang, S.; Lu, S.; Dong, Z.; Yang, J.; Yang, M.; Zhang, Y. Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection. Appl. Sci.
**2016**, 6, 169. [Google Scholar] [CrossRef] - Kumar, S.; Dabas, C.; Godara, S. Classification of Brain MRI Tumor Images: A Hybrid Approach. Procedia Comput. Sci.
**2017**, 122, 510–517. [Google Scholar] [CrossRef] - Abiwinanda, N.; Hanif, M.; Hesaputra, S.T.; Handayani, A.; Mengko, T.R. Brain Tumor Classification Using Convolutional Neural Network. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Prague, Czech Republic, 3–8 June 2018; pp. 183–189. [Google Scholar]
- Khan, H.A.; Jue, W.; Mushtaq, M.; Mushtaq, M.U. Brain tumor classification in MRI image using convolutional neural network. Math. Biosci. Eng.
**2020**, 17, 6203–6216. [Google Scholar] [CrossRef] - Hossain, T.; Shishir, F.S.; Ashraf, M.; Al Nasim, M.A.; Shah, F.M. Brain Tumor Detection Using Convolutional Neural Network. In Proceedings of the 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 3–5 May 2019; pp. 1–6. [Google Scholar]
- Gattim, N.K.; Rajesh, V. Rotation and Scale Invariant Feature Extraction for MRI Brain Images. JATIT & LLS
**2014**, 70, 62–67. [Google Scholar] - Selvaraj, D.; Dhanasekaran, R. A Review on Tissue Segmentation and Feature Extraction of MRI Brain images. Int. J. Comput. Sci. Eng. Technol. (IJCSET)
**2013**, 4, 1313–1332. [Google Scholar] - Qurat-Ul-Ain, G.L.; Kazmi, S.B.; Jaffar, M.A.; Mirza, A.M. Classification and Segmentation of Brain Tumor using Texture Analysis. Recent Adv. Artif. Intell. Knowl. Eng. Data Bases
**2010**, 10, 147–155. [Google Scholar]

**Figure 3.**Here, image (

**a**) is an input T-2 brain MRI image, whereas (

**b**) is a segmented T-2 image. It is a Malignant Tumor according to the segmented picture.

**Figure 4.**Here, image (

**a**) is the input T-1 brain MRI image and image (

**b**) is the segmented T-1 image. A segmented image classifies it as a Benign Tumor.

**Figure 5.**Here, image (

**a**) is an input distorted/simulated (rotated and scaled) brain MRI image and image (

**b**) is a segmented simulated (rotated and scaled) image. A segmented image classifies it as a Malignant Tumor.

**Figure 10.**The percentage of accuracy versus image for distorted/simulated image: (

**a**) RBF; (

**b**) Linear (

**c**) Polynomial; (

**d**) Quadratic.

**Figure 13.**The accuracy percentages for the T-1 weighted images: (

**a**) RBF; (

**b**) Linear; (

**c**) Polynomial; (

**d**) Quadratic.

Classification | Simulated Images | |||
---|---|---|---|---|

Tumor Classification | Training | Validation | ||

Benign | Malignant | Benign | Malignant | |

9 | 7 | 2 | 2 |

Classification | T-2 Weighted Images | |||
---|---|---|---|---|

Tumor Classification | Training | Validation | ||

Benign | Malignant | Benign | Malignant | |

18 | 20 | 4 | 6 |

Classification | T-1 Weighted Images | |||
---|---|---|---|---|

Tumor Classification | Training | Validation | ||

Benign | Malignant | Benign | Malignant | |

13 | 5 | 4 | 2 |

Classification | Method | Simulated Images | |||
---|---|---|---|---|---|

RBF (%) | Linear (%) | Polynomial (%) | Quadratic (%) | ||

Abnormality Classification | DWT | 83.29 | 85.97 | 86.37 | 87.49 |

DWT + LPT | 89.41 | 91.11 | 90.21 | 91.78 | |

DWT + LPT + CNN | 92.41 | 99.11 | 98.21 | 99.78 | |

Tumor Classification | DWT | 64.29 | 71.43 | 60.71 | 71.43 |

DWT + LPT | 78.57 | 79.64 | 82.14 | 78.57 | |

DWT + LPT + CNN | 89.41 | 86.11 | 88.21 | 89.78 |

Classification | Method | T-1 Weighted Images | |||
---|---|---|---|---|---|

RBF (%) | Linear (%) | Polynomial (%) | Quadratic (%) | ||

Abnormality Classification | DWT | 89.29 | 93.75 | 95.09 | 94.94 |

DWT + LPT | 92.71 | 96.40 | 97.66 | 96.21 | |

DWT + LPT + CNN | 94.84 | 98.62 | 98.28 | 98.45 | |

Tumor Classification | DWT | 75.00 | 83.33 | 89.58 | 83.33 |

DWT + LPT | 85.42 | 89.58 | 93.75 | 93.75 | |

DWT + LPT + CNN | 91.48 | 94.83 | 97.66 | 97.66 |

Classification | Method | T-2 Weighted Images | |||
---|---|---|---|---|---|

RBF (%) | Linear (%) | Polynomial (%) | Quadratic (%) | ||

Abnormality Classification | DWT | 89.51 | 94.20 | 94.53 | 94.59 |

DWT + LPT | 92.02 | 96.94 | 95.88 | 97.72 | |

DWT + LPT + CNN | 94.21 | 97.23 | 97.26 | 98.87 | |

Tumor Classification | DWT | 77.90 | 73.16 | 77.90 | 81.05 |

DWT + LPT | 80.53 | 81.05 | 82.63 | 84.74 | |

DWT + LPT + CNN | 91.33 | 93.85 | 95.67 | 97.28 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Jibon, F.A.; Khandaker, M.U.; Miraz, M.H.; Thakur, H.; Rabby, F.; Tamam, N.; Sulieman, A.; Itas, Y.S.; Osman, H.
Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation. *Healthcare* **2022**, *10*, 1801.
https://doi.org/10.3390/healthcare10091801

**AMA Style**

Jibon FA, Khandaker MU, Miraz MH, Thakur H, Rabby F, Tamam N, Sulieman A, Itas YS, Osman H.
Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation. *Healthcare*. 2022; 10(9):1801.
https://doi.org/10.3390/healthcare10091801

**Chicago/Turabian Style**

Jibon, Ferdaus Anam, Mayeen Uddin Khandaker, Mahadi Hasan Miraz, Himon Thakur, Fazle Rabby, Nissren Tamam, Abdelmoneim Sulieman, Yahaya Saadu Itas, and Hamid Osman.
2022. "Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation" *Healthcare* 10, no. 9: 1801.
https://doi.org/10.3390/healthcare10091801