Deep learning based brain tumor detection via fuzzy hexagonal membership function

Brain tumor is an anomalous growth of brain cells. Segmentation of brain tumors is currently the most important surgical and pharmaceutical procedure. However, manually segmenting the brain tumor is a challenging task due to the complex structure of brain. In recent years, artificial intelligence te...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 45; no. 2; pp. 2979 - 2992
Main Authors Kala, R., Deepa, P.
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 01.01.2023
Subjects
Accuracy
Artificial intelligence
Brain
Brain cancer
Deep learning
Fallout
Fuzzy logic
Image segmentation
Machine learning
Magnetic resonance imaging
Medical imaging
Performance measurement
Tumors
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Summary:Brain tumor is an anomalous growth of brain cells. Segmentation of brain tumors is currently the most important surgical and pharmaceutical procedure. However, manually segmenting the brain tumor is a challenging task due to the complex structure of brain. In recent years, artificial intelligence techniques with the fuzzy logic have shown better results in the field of medicine. In this work, a novel deep learning classification network with fuzzy hexagonal membership function (DLC-FHMF) model has been proposed for accurately segmenting brain tumors. The different MRI modalities namely T1, T1-c, T2 and Flair images are preprocessed using a fuzzy hexagonal trilateral and median filter to eliminate the Rician noise. Afterwards, the DLC-FHMF model is used for segmenting the tumor portion by using the multimodal composition of MRI as input. The fuzzy weights are determined with hexagonal membership functions and convoluted with the corresponding MRI images. The quantitative examination is carried out using the performance metrics namely accuracy, specificity, precision, sensitivity, incorrect segmentation, under-segmentation, and over-segmentation. In addition to the above metrics, the pre-processing metrics include PSNR, RMSE, and SSIM. The experimental fallout portrayals that the proposed DLC-FHMF approach attains a better accuracy range of 99% for detecting brain tumors using the BRATS 2013 dataset. The proposed DLC-FHMF model improves the overall accuracy by 15.1%, 11.1%, 3.0%, 21.2% and 0.5% better than ANN, SVM, NB, DNN and DAE respectively.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-221990