Neural Gas Network-Based Distinction Between Brain Cysts and Tumors from Magnetic Resonance Images Using a Hybrid Deep Learning Algorithm.

Abstract

The early and accurate diagnosis of brain tumors is critically important, as timely intervention significantly reduces mortality and improves patient outcomes. While magnetic resonance imaging (MRI) is the preferred diagnostic tool, differentiating malignant brain tumors from benign cysts remains a clinical challenge due to overlapping visual features. This study introduces a technically novel hybrid deep learning framework that integrates denoising, segmentation, and classification stages in a unified pipeline. First, a neural gas network is employed for precise segmentation of MRI images, enhancing boundary delineation between cystic and tumorous regions. To mitigate class imbalance and improve generalization, the Synthetic Minority Oversampling Technique is applied. A unique architectural contribution of this study lies in the integration of three pretrained convolutional neural networks (CNNs)-EfficientNetB0, MobileNetV2, and ResNet50-into a fused hybrid model that combines their complementary feature extraction capabilities. This model is fine-tuned using a grid search-based hyperparameter optimization strategy, early stopping, and cross-validation. Comparative experiments on four distinct datasets (including balanced and segmented versions) demonstrate that the proposed hybrid model consistently outperforms conventional models such as VGG16, InceptionV3, and individual CNN baselines. Achieving a peak accuracy of 97% on the balanced, augmented, and segmented dataset, this work highlights a novel and robust Artificial Intelligence (AI)-based strategy for distinguishing brain cysts from tumors in MRI, with superior performance in complex diagnostic scenarios.