Epistemological Examination of Algorithmic Prowess in Neuro-Oncological Diagnostics: An Erudite Comparative Analysis of VGG16, VGG19, ResNet50, and DenseNet201 Deep Architectonics

Abstract

Brain tumors pose a significant global health challenge, contributing to high morbidity and mortality rates among both children and adults. The need for early and precise detection is critical, as it directly affects treatment outcomes and survival rates. This study evaluates the performance of five advanced deep learning models-Convolutional Neural Network (CNN), VGG16, VGG19, ResNet50, and DenseNet201-in classifying brain tumors using a dataset of brain MRI images. The models were assessed based on their accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. DenseNet201 achieved the highest accuracy of 99.83%, with a recall of 99.70% and an AUC of 99.99%. However, its higher computational cost and memory usage result in a slower inference time, making it less feasible for real-time applications. In contrast, VGG16 provided a balanced performance with an accuracy of 98.83%, a recall of 98.93%, and an AUC of 99.30%, while also offering faster inference times, making it the most efficient model for practical use. CNN, VGG19, and ResNet50 also performed well, with accuracy scores of 97.0%, 96.67%, and 90.33%, respectively, and strong AUC scores. These results underscore the potential of VGG16 in enhancing early detection and accurate classification of brain tumors, providing a faster and more practical solution. Future research should focus on optimizing these models and validating their use across various clinical environments to improve diagnostic accuracy and patient outcomes.

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