A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification. Issue 1 (13th July 2020)

• Record Type: Journal Article
• Authors: Ragupathy, Balakumaresan; Karunakaran, Manivannan
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 1(2021)
• Page Start: 379
• Page End: 390
• Publication Date: 2020-07-13
• Subjects: brain -- classifications -- features -- meningioma -- tumor; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22464
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.