A Novel Supervised Learning Model for MRI Image Using Fuzzy Clustering and Naïve Bayes Algorithms

Abstract

This study introduces a novel supervised learning algorithm with multiple enhancements designed specifically for MRI image analysis. After MRI images preprocessing, we use fuzzy clustering algorithm to segment them. This step helps to highlight the differences between pixel groups in different clusters. Once the dataset is enhanced, we extract the structural features from each image and estimate the probability density functions that represent each cluster. The prior probabilities of classified images are then determined, and the images are categorized using the Naïve Bayes method. The authors developed an efficient supervised learning algorithm for MRI image analysis building on these innovations. The proposed algorithm can be implemented on MATLAB. When tested on cancer imaging datasets, it consistently outperformed other classification methods like ResNet50 and VGG16, demonstrating its effectiveness and reliability.

Keywords

image segmentation fuzzy clustering Naïve Bayes Brain tumor MRI images classification

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References

Ali

Hamraz

Gul

Khan

D. M.

Aldahmani

Khan

(2023). A k nearest neighbour ensemble via extended neighbourhood rule and feature subsets. Pattern Recognition, 142, 109641. https://doi.org/10.1016/j.patcog.2023.109641

Bansal

Goyal

Choudhary

(2022). A comparative analysis of k-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decision Analytics Journal, 3(1), 100071. https://doi.org/10.1016/j.dajour.2022.100071

Dinh

P.-T.

Tai

V.-V.

(2024). Building the classification model based on the genetic algorithm and the improved Bayesian method. International Journal of Data Science and Analytics, 18(4), 405–421. https://doi.org/10.1007/s41060-023-00436-2

El Boustani

Aatila

El Bachari

El Oirrak

(2020). MRI Brain images classification using convolutional neural networks. In Ezziyyani

(Ed.), Advanced intelligent systems for sustainable development. AI2SD 2019. Advances in intelligent systems and computing. Springer International Publishing (pp. 1105). https://doi.org/10.1007/978-3-030-36674-2_32

Fang

(2022). Naive Bayes image classification based on multiple features. Computer Software and Media Applications, 5(1), 12-15. https://doi.org/10.24294/csma.v2i1.1171

Huang

Cai

Pacheco

P. P.

Narrandes

Wang

(2018). Applications of support vector machine learning in cancer genomics. Cancer Genomics & Proteomics, 15(1), 41–51. https://doi.org/10.21873/cgp.20063

Hussain

Bird

J. J.

Faria

D. R.

(2019). A study on cnn transfer learning for image classification. In Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Springer, pp. 191–202.

Lethikim

Nguyentrang

Vovan

(2022). A new image classification method using interval texture feature and improved Bayesian classifier. Multimedia Tools and Applications, 81(25), 36473–36488. https://doi.org/10.1007/s11042-022-13531-6

Tjostheim

(2014). Nonparametric estimation of probability density functions for irregularly observed spatial data. Journal of the American Statistical Association, 109(508), 1546–1564. https://doi.org/10.1080/01621459.2014.947376

10.

Luo

Huang

Liu

(2023). Classification of tumor in one single ultrasound image via a novel multi-view learning strategy. Pattern Recognition, 143, 109776. https://doi.org/10.1016/j.patcog.2023.109776

11.

Nhu

V. H.

Zandi

Shahabi

Chapi

Shirzadi

Al-Ansari

Singh

S. K.

Dou

Nguyen

(2020). Comparison of support vector machine, Bayesian logistic regression, and alternating decision tree algorithms for shallow landslide susceptibility mapping along a mountainous road in the west of Iran. Applied Sciences, 10(15), 5047. https://doi.org/10.3390/app10155047

12.

Saeedi

Rezayi

Keshavarz

Niakan Kalhori

S. R.

(2023). MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Medical Informatics and Decision Making, 23(1), 16. https://doi.org/10.1186/s12911-023-02114-6

13.

Sagi

Rokach

(2021). Approximating xgboost with an interpretable decision tree. Information Sciences, 572, 522–542. https://doi.org/10.1016/j.ins.2021.05.055

14.

Sankaranarayaanan

Kumar

M. S.

Chidhambararajan

Sirenjeevi

(2023, January). Brain tumor detection and classification using VGG 16. In 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, pp. 1–5.

15.

Sharma

A. K.

Nandal

Dhaka

Zhou

Alhudhaif

Alenezi

Polat

(2023). Brain tumor classification using the modified ResNet50 model based on transfer learning. Biomedical Signal Processing and Control, 86, 105299. https://doi.org/10.1016/j.bspc.2023.105299

16.

Singh

Kumar

(2024). Brain MRI image analysis for Alzheimer’s disease (AD) prediction using deep learning approaches. SN Computer Science, 5(1), 160. https://doi.org/10.1007/s42979-023-02461-1

17.

Singhal

Gupta

Singhal

(2022). An ensembling approach using DL & non-DL techniques for detecting brain tumors using MRI scans. In 2022 IEEE international power and renewable energy conference (pp. 1–6). https://doi.org/10.1109/IPRECON55716.2022.10059480

18.

Song

Wang

Zhao

(2023). Doubly robust logistic regression for image classification. Applied Mathematical Modelling, 123, 430–446. https://doi.org/10.1016/j.apm.2023.06.039

19.

Tabrizchi

Parvizpour

Razmara

(2023). An improved vgg model for skin cancer detection. Neural Processing Letters, 55(4), 3715–3732. https://doi.org/10.1007/s11063-022-10927-1

20.

Tang

Wang

Kwong

(2017). G-ms2f: Googlenet based multi-stage feature fusion of deep cnn for scene recognition. Neurocomputing, 225, 188–197. https://doi.org/10.1016/j.neucom.2016.11.023

21.

Feng

Lin

Chen

Qiu

Liu

Zhang

(2023). Ctranscnn: Combining transformer and cnn in multilabel medical image classification. Knowledge-Based Systems, 281, 111030. https://doi.org/10.1016/j.knosys.2023.111030

22.

Pan

Zhu

Cai

Zhang

(2015). Self-adaptive attribute weighting for naive Bayes classification. Expert Systems with Applications, 42(3), 1487–1502. https://doi.org/10.1016/j.eswa.2014.09.019

23.

Younis

Qiang

Afzal

Adamu

M. J.

Kawuwa

H. B.

Hussain

(2024). Abnormal brain tumors classification using resnet50 and its comprehensive evaluation. IEEE Access (12), 78843–78853. https://doi.org/2010.1109/ACCESS.2024.3403902

24.

Zhang

Y. D.

Govindaraj

V. V.

Tang

Zhu

Sun

(2019). High performance multiple sclerosis classification by data augmentation and alexnet transfer learning model. Journal of Medical Imaging and Health Informatics, 9(9), 2012–2021. https://doi.org/10.1166/jmihi.2019.2692