Mathematical Modeling and Statistical Analysis of Novel Hybrid Deep Learning Models for Efficient Demarcation and Classification of Brain Tumors
Year: 2024
Type: article
Abstract: Recent advancements in medical imaging and artificial intelligence have significantly enhanced the diagnostic capabilities for brain tumors, yet challenges persist in achieving precise tumor demarcation and classification. This research paper introduces a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) to improve the accuracy and efficiency of brain tumor identification and categorization from magnetic resonance imaging (MRI) data. The proposed model leverages the spatial feature extraction capabilities of CNNs and the sequential data processing strength of RNNs, aiming to address the complexity and variability of tumor appearances and locations.Mathematical modeling of the hybrid architecture is presented, detailing the layer configurations, activation functions, and the integration mechanism between CNN and RNN layers. The model is trained and validated using a substantial dataset of MRI images, which includes a diverse range of tumor types and stages, sourced from publicly available medical databases. Statistical analysis is employed to evaluate the model's performance, focusing on metrics such as accuracy, precision, recall, and F1-score, against existing deep learning models. Results indicate that the hybrid model significantly outperforms traditional single-architecture models in both demarcation precision and classification accuracy. The integration of CNN with RNN not only enhances the detection of intricate tumor boundaries but also improves the classification of tumor types based on their morphological and intensity features. This research contributes to the fields of medical imaging and artificial intelligence by providing a robust mathematical model and empirical evidence of the efficacy of hybrid deep learning systems in medical diagnostics, particularly in the challenging area of brain tumor analysis.
Author: Mukesh Chand
Institution: Poornima University
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4407040290