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A new intelligent system for diagnosing tumors with MR images using type-2 fuzzy neural network (T2FNN)

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Abstract

Early diagnosis of tumors can reduce mortality rates. Hence, tumor position, tumor area, and tumor categories evaluation are also mandatory concerns for the proper medication. This paper presents a new intelligent system for diagnosing the human brain tumors using 60 magnetic resonance images (MRI) with contrast. The proposed methods have five distinct modules including pre-processing, performance elements, critic, learning element, and classification. In the pre-processing, the quality of MR images are enhanced and the noises are removed from it. In the performance elements, the images are segmented with K-mean algorithm and the feathers are extracted from the images with the help of gray level co-occurrence matrix. Next, the data is manipulated in critical part with roles and it is transfered to the learning element part. Then, the Self Organizing Map (SOM) is used to identify the exact location of tumors. Finally, the four types of tumors, astrocytoma, meningiomas, metastatic and glioblastoma will be classified by the K-mean type-2 fuzzy neural. The obtained results indidate that the proposed method has greater values of Sensitivity, Precision, F-measure, Accuracy, and Receiver Operating Characteristic (ROC) compare to other relevant methods.

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Authors and Affiliations

  1. Department of Industrial Engineering, Yazd University, Yazd, Iran

    Vahid Rezaie

  2. Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

    Amir Parnianifard

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  1. Vahid Rezaie
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Appendices

Appendix 1

The proposed model, defined in Sect. 2, is used to design software called MRI Tumor Detection and Classification. This system includes six major parts. By clicking “Load MR images”, the desired image will be selected. The preprocessing key prepares the image and segmentation key determines the tumor location. Next, by clicking “extract feature”, 12 feature will be shown. Fz2 Classification and Analysis keys determined the tumor type, accuracy, and execution time (Fig. 

Fig. 11
figure 11

MRI Tumor Detection and Classification software

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11). The Brain Tumor Detection Toolbox and some MRI resources can be found in Google Drive.

Appendix 2

Table 4 Possible outcomes as in the confusion matrix with 5%,7%, and 9% of salt and pepper noise with 20% inhomogeneity
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Table 5 Possible outcomes as in the confusion matrix with 5%,7%, and 9% of salt and pepper noise with 40% inhomogeneity
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Table 6 Performance of the classifier with 5%,7%, and 9% of salt and pepper noise with 20% inhomogeneity
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Table 7 Performance of the classifier with 5%, 7%, and 9% of salt and pepper noise with 40% inhomogeneity
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Rezaie, V., Parnianifard, A. A new intelligent system for diagnosing tumors with MR images using type-2 fuzzy neural network (T2FNN). Multimed Tools Appl 81, 2333–2363 (2022). https://doi.org/10.1007/s11042-021-11221-3

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