MHNFSP: Design of an Efficient Model for Advanced MRI Image Classification Using Hybrid Neural Networks and Optimized Feature Selection Process

In the field of medical imaging, particularly regarding magnetic resonance imaging (MRI) and the analysis of feature extraction in the MRI, there exists an immediate and pressing need for precision and clarity in the accuracy of such. Current methods, though useful, have inherent weaknesses with respect to dealing with noise, specific feature identification, and an ability to effectively merge different types of data. The explained developments show the need of innovation in MRI image classification, especially for distinguishing slight but critical variations in brain images and samples. These gaps underline the urgency for innovation in MRI image classification, especially for differentiating delicate nuances of brain images and samples. The proposed model has addressed these gaps through an innovative workflow for MRI image classification, identified by a novel, synergistic approach process. The workflow begins with the scrupulous acquisition of MRI and fMRI images, capturing the intricate structural and functional nuances of the brain. Image segmentation is employed for innovative execution using Fuzzy C-Means (FCM), picked for its excellent noise resistance and the advantage of soft clustering that is important for a more exact definition of the boundaries of the regions of interest in the image set. The feature extraction phase involves three phases with Fourier, Entropy, and Convolution features from an elevated perspective. This trio is very carefully picked to encapsulate a comprehensive image representation, harmonizing the shape, texture, and local structure. The workflow of this model is streamlined from the feature set with the employment of Whale Optimization, which quite efficaciously sieves the feature set, retaining the most informative while curtailing redundancy and overfitting issues within the process. Eventually, a Hybrid Convolutional Neural Network (CNN) emerges at the top of this model, incorporating Naive Bayes, k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) methods. This selective multi-classifier classification provides a robust and heterogeneous set of functions for the classification. Tested on different OASIS and ADNI datasets and samples, this model has outperformed current methods with a 4.9% increase in precision, a 4.5% enhancement in accuracy, a 3.5% rise in recall, a 4.3% augmentation in AUC (Area Under the Curve), a 3.4% improvement in specificity, and a 2.9% cutdown in delay. Such advancements not only signify a great leap in MRI image classification but also carry very much significance for medical diagnosis and research, and that could see its way into the clinical field. In this model, the amalgamation of disparate methods sets a new benchmark in medical imaging, opening doors to better accuracy, higher efficiency, and greater reliability of diagnostic tools.