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

Summary

This study proposes an optimized brain tumor detection method by integrating and evaluating five pretrained models. The approach achieves enhanced accuracy and generalization by utilizing advanced image augmentation, hyperparameter tuning, and various optimizers, offering significant contributions to medical imaging for brain tumor diagnosis.

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.

Introduction

Brain tumors, whether benign or malignant, pose a significant threat to human health and well-being. The timely detection of these tumors plays a pivotal role in determining patient outcomes, making it a priority in the medical field^1,39(also see Supplemental Table S1). Detecting brain tumors at an early stage is not only a vital medical objective but also a crucial lifeline for patients. By enabling timely intervention and access to effective treatment options, it fosters improved prognosis, empowers patients, and reduces the overall burden of this challenging condition<....

Protocol

Figure 7 illustrates a schematic block diagram representing the overall methodology of the proposed approach. In the suggested method, MRI scan images of brain tumors are preprocessed using two popular techniques-normalization and scaling. Some popular pretrained transfer learning models like VGG16, MobileNetV2, DenseNet121, ResNet50, and InceptionV3 are utilized in this work. After training, the models are tested on hypothetical cases, and an extensive analysis is done to check the models' performance based on various performance metrics such as accuracy, recall, precision, and F1-score. The proposed technique uses deep learning to e....

Results

The performance evaluation of each of the five models-VGG16, MobileNetV2, DenseNet121, InceptionV3, and ResNet50-includes a detailed analysis with accuracy curves, confusion matrices, and classification reports. These metrics and visualizations are important tools for evaluating the models' proficiency in brain tumor detection. The models' learning progress over epochs is dynamically represented by the accuracy curves, which also shed light on possible overfitting and convergence of the m.......

Discussion

Model performance

The effective use of deep learning models (pretrained) is the primary reason for the strong performance in brain tumor identification. A combination of prominent architectures used to evaluate the performance are VGG16, MobileNetV2, DenseNet121, InceptionV3, and ResNet50. These models have impressive feature extraction abilities since they underwent pretraining using extensive collections of image data. The weights have been .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
GPU Infrastructure and Jupyter Notebook	Kaggle (Online Platform Provider)	Tesla-P100	GPU infrastructure provided by Kaggle’s Jupyter Notebook service, utilizing the latest NVIDIA Tesla P100 GPU
Intel Core i5-8350U	Intel	i5-8350U	Intel Core i5-8350U CPU @ 1.70GHz (1.90 GHz max) for local processing
RAM	Standard	8GB_RAM	8 GB of RAM for data processing
Operating System	Microsoft	Windows_64	64-bit Windows OS for local processing
Python Libraries	Python Community	N/A	NumPy, scikit-learn, PIL, TensorFlow 2.12, Keras 2.12