Augmented Multimodal Fusion for Optimized Brain Tumor Detection: Evaluation and Comparative Analysis.

Abstract

Brain tumors represent a significant medical challenge, necessitating accurate and efficient detection methods for timely intervention. This work integrates several pretrained base models, such as VGG16, MobileNetV2, DenseNet121, InceptionV3, and ResNet50, to propose a novel method for brain tumor diagnosis. A streamlined and standardized technique has been proposed to accommodate various base models, ensuring consistency and ease of maintenance and facilitating model comparison. To amplify the variety of the training dataset and enhance model generalization, notable image augmentation methods like adjusting brightness and contrast are utilized. Further, an effective training pipeline utilizing data generators is designed to process large datasets efficiently while conserving computing power. The study conducted a thorough analysis using three different optimizers (Adam, Stochastic Gradient Descent, and Adamax) applied to each pretrained base model, with comprehensive adjustments of hyperparameters. Metrics like recall, accuracy, precision, F1-score, and confusion matrices are used to evaluate the model's performance, providing a comprehensive understanding of the model's behavior. A systematic comparison of each model's performance provided an in-depth examination of strengths and weaknesses, facilitating informed model selection and decision-making for brain tumor detection applications. MobileNetV2 achieved the highest overall performance with an accuracy of 96%, precision of 96%, recall of 94%, and an F1-score of 95% using the Adam optimizer. DenseNet121 and VGG16 also performed well, achieving accuracies of 95% and 94%, respectively. InceptionV3 demonstrated a slightly lower performance compared to the top-performing models, with an accuracy of 93%, precision of 93%, recall of 91%, and an F1-score of 92%. ResNet50 showed relatively lower performance with an accuracy of 77%, precision of 78%, recall of 76%, and an F1-score of 76%. These metrics demonstrate the robustness and efficacy of the proposed method for brain tumor detection.