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.
AliA.HamrazM.GulN.KhanD. M.AldahmaniS.KhanZ. (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
2.
BansalM.GoyalA.ChoudharyA. (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
3.
DinhP.-T.TaiV.-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
4.
El BoustaniA.AatilaM.El BachariE.El OirrakA. (2020). MRI Brain images classification using convolutional neural networks. In EzziyyaniM. (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
5.
FangK. (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
6.
HuangS.CaiN.PachecoP. P.NarrandesS.WangY.XuW. (2018). Applications of support vector machine learning in cancer genomics. Cancer Genomics & Proteomics, 15(1), 41–51. https://doi.org/10.21873/cgp.20063
7.
HussainM.BirdJ. J.FariaD. 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.
8.
LethikimN.NguyentrangT.VovanT. (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
9.
LuZ.TjostheimD. (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.
LuoY.HuangQ.LiuL. (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.
NhuV. H.ZandiD.ShahabiH.ChapiK.ShirzadiA.Al-AnsariN.SinghS. K.DouJ.NguyenH. (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.
SaeediS.RezayiS.KeshavarzH.Niakan KalhoriS. 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
SankaranarayaananR.KumarM. S.ChidhambararajanB.SirenjeeviP. (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.
SharmaA. K.NandalA.DhakaA.ZhouL.AlhudhaifA.AleneziF.PolatK. (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.
SinghA.KumarR. (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.
SinghalR.GuptaS.SinghalP. (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
TabrizchiH.ParvizpourS.RazmaraJ. (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.
TangP.WangH.KwongS. (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.
WuX.FengY.XuH.LinZ.ChenT.LiS.QiuS.LiuQ.MaY.ZhangS. (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.
WuJ.PanS.ZhuX.CaiZ.ZhangP.ZhangC. (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.
YounisA.QiangL.AfzalZ.AdamuM. J.KawuwaH. B.HussainF.HussainH. (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.
ZhangY. D.GovindarajV. V.TangC.ZhuW.SunJ. (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
