WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 23, 2024
Innovative Convolutional Neural Network Hybrid for Brain Tumor Segmentation
Authors: , , , , , ,
Abstract: Brain tumor segmentation and classification play vital roles in the treatment, diagnosis, therapy
planning, and evaluation of responses to cancer treatment. Magnetic resonance imaging (MRI) is a prevalent
method for analyzing brain tumors, utilizing various acquisition protocols, including both conventional and
advanced techniques. This study introduces a fully automated model designed to segment abnormal tissues related
to brain tumors from multimodal MRI images, thereby assisting radiologists in diagnosis and treatment planning.
The proposed model, termed "CNN-FCM," combines a hybrid convolutional neural network with fuzzy c-means
clustering to tackle the challenges posed by inaccurate segmentation and diverse input image dimensions. The
architecture of the model consists of three layers: a convolutional layer, a ReLU activation layer, and a clustering
layer. Comprehensive experiments conducted on the BRATS 2017 dataset reveal that the model delivers
competitive outcomes, achieving performance metrics such as accuracy, sensitivity, specificity, overall Dice
coefficient, and recall of 96.5%, 95.21%, 94.8%, 93.7%, and 94.1%, respectively. These findings suggest that the
hybrid CNN-FCM model effectively resolves issues related to segmentation accuracy and variability in input
images within the context of brain tumor analysis is.
Search Articles
Keywords: Brain tumor, Convolutional neural network, MRI dataset, segmentation, FCM algorithm, and
accuracy value
Pages: 252-261
DOI: 10.37394/23205.2024.23.25