Hybrid Parallel Fuzzy CNN Paradigm: Unmasking Intricacies for Accurate Brain MRI Insights

The Hybrid Parallel Fuzzy CNN (HP-FCNN) is a ground-breaking method for medical image analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a convolutional neural network (CNN). This novel combination tackles problems related to brain image processing, reducing problems such as noise and hazy borders that are common in Magnetic Resonance Imaging (MRI). Unlike other CNN models, HP-FCNN combines fine-grained fuzzy representations with crisp CNN features, improving interpretability by displaying hidden layers. This insight into activation patterns facilitates comprehension of the decision-making processes necessary for the diagnosis of brain diseases. HP-FCNN outperforms other pretrained models (ResNet, DenseNet, VGG, and EfficientNet) on measures such as the confusion matrix and AUC-ROC, according to comparative assessments. Furthermore, the addition of Adaptive Class Activation Mapping (AD-CAM) enhances HPFCNN by identifying salient features during backpropagation and bolstering the network's capacity to enhance brain illness diagnosis and treatment planning. Our methodology, incorporating AD-CAM, yielded compelling results with a 96.86 F1-Score, 96.41 AUC, and 96.81 Accuracy, showcasing the effectiveness of our approach in achieving high-performance metrics in brain MRI analysis. With a 15% increase in accuracy, a 10% increase in sensitivity, and a 12% decrease in false positives, HP-FCNN outperforms its predecessors. These impressive advancements represent a quantifiable breakthrough in the capabilities of medical image processing technology; they are more than just anecdotal evidence.