A computer-aided brain tumor diagnosis by adaptive fuzzy active contour fusion model and deep fuzzy classifier

• Published in: Multimedia tools and applications Vol. 81; no. 18; pp. 25405 - 25441
• Main Authors: Kumar, Katukuri Arun, Boda, Ravi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Brain; Brain cancer; Classification; Classifiers; Computer Communication Networks; Computer Science; Contours; Data Structures and Information Theory; Diagnosis; False alarms; Feature extraction; Fuzzy logic; Image classification; Image enhancement; Image quality; Image segmentation; Machine learning; Medical imaging; Multimedia Information Systems; Parameter modification; Special Purpose and Application-Based Systems; Tumors; Brain tumor classification; Skull stripping; Adaptive fuzzy active contour fusion model; Jaya-tunicate swarm algorithm; Modified convolutional neural network (CNN)-fuzzy classifier
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12213-7

Brain tumor classification is a significant issue in Computer-Aided Diagnosis (CAD) for clinical applications. The classification process is crucial and plays a major role to diagnosis the brain tumors. The existing works focus on recognizing brain tumors through diverse classification approaches. Though, the conventional classification approaches are suffered from high false alarm rates. To improve the early-stage brain tumor diagnosis via classification, the main intention of this paper is to introduce a novel brain tumor segmentation and classification model. The dataset gathered from the two benchmark sources is subjected to pre-processing for enhancing the quality of images, and skull stripping for extracting the region of interest from the skull. Further, a new segmentation approach termed Adaptive Fuzzy Active Contour Fusion Model (AFACFM) with a new fitness function is developed. Here, the enhancement of the segmentation is performed by the hybrid Jaya-Tunicate Swarm Algorithm (J-TSA). Next, the combination of Convolutional Neural Network (CNN) and Fuzzy classifier is performed in the final classification phase. The deep features are extracted from the pooling layer of CNN, which are subjected to the Fuzzy classifier for classifying the images into normal, benign, and malignant. As a modification, the parameters of the CNN and Fuzzy classifier are tuned by the proposed J-TSA. The comparative analysis is finally done, and this work demonstrates the potential of using deep learning in MRI images to provide a non-invasive tool for simultaneous and automated tumor segmentation and classification. Through the performance analysis, the accuracy of the designed CNN-Fuzzy using J-TSA was 77%, 29%, 19%, 8.7%, 6.8%, and 1.6% enhanced than SVM, NN, DBN, CNN, Fuzzy, and CNN-Fuzzy, respectively.