Innovative Convolutional Neural Network Hybrid for Brain Tumor Segmentation

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.