Brain Tumor Detection Disease Prediction

Abstract

Detecting brain tumors in human brain MRI scans is a crucial yet tough task that carries significant implications for clinical practice. This study delves into using deep learning algorithms, particularly EfficientNet B5, to accurately and automatically spot brain tumors. By tapping into the capabilities of image processing and convolutional neural networks, our approach aims to create a strong and efficient solution. We work with a diverse set of MRI scans, covering different brain tumor stages and types, to train and test the model\'s effectiveness. The main contributions of our research involve building a thorough process for preparing MRI data, fine-tuning the EfficientNet B5 structure, and conducting a detailed analysis of how well the model performs. Our results showcase promising levels of accuracy, sensitivity, and specificity in categorizing brain tumors, highlighting the potential of deep learning algorithms to aid clinicians in swiftly and precisely diagnosing brain tumors. This could significantly bolster patient care and treatment strategies.

Introduction

As the global population ages, brain tumors—complex neurological conditions—are becoming a growing concern. Early and accurate diagnosis is essential, and integrating Magnetic Resonance Imaging (MRI) with deep learning, especially EfficientNet B5, has shown promise. EfficientNet B5 offers a balance between high accuracy and computational efficiency, excelling in detecting subtle tumor indicators from MRI data. It also helps understand tumor progression and supports personalized treatment strategies.

Literature Review

Recent research highlights deep learning's role in brain tumor detection:

• 3D CNNs (Shi et al.) and transfer learning (Han et al.) have enhanced diagnostic accuracy.

• Advanced methods such as GANs, multi-modal MRI fusion, and ensemble models have improved rare tumor detection and generalization.

• EfficientNet variants and hybrid models (e.g., EfficientNet + U-Net) have shown strong clinical applicability.

Existing Systems

Traditional methods (KNN, Naïve Bayes) and early CNNs lack the accuracy and interpretability needed for medical use (often under 90% accuracy). Challenges include:

• Tumor subtype variability

• Heterogeneous MRI data

• Limited labeled datasets

• Low interpretability

• Ethical and privacy concerns

Proposed Solution

The proposed system uses EfficientNet B5 enhanced by:

• Preprocessing (noise removal, normalization)

• Transfer learning from datasets like ImageNet

• Data augmentation for training diversity

• Ensemble learning to improve robustness

• Interpretability tools like class activation maps (CAMs)

• Clinical integration with user-friendly tools for diagnosis and monitoring

• Mental health link detection, exploring stress correlations with tumors

This holistic approach aims to deliver high-accuracy, early detection of brain tumors while being practical for clinical use.

Results

The model showed:

• High classification accuracy, precision, and recall

• Strong generalization across noisy or variable MRI inputs

• Effective tumor detection in clinical conditions

Conclusion

In conclusion, the application of the EfficientNet B5 algorithm for brain tumor detection through the analysis of MRI images of the human brain represents a significant advancement in the field of medical imaging and neurology. Brain tumor, a complex and devastating cognitive disorder, presents a growing global healthcare challenge, making early and accurate diagnosis crucial for effective intervention and treatment. The utilization of deep learning algorithms like EfficientNet B5 has demonstrated remarkable potential in improving the efficiency and accuracy of brain tumor diagnosis. EfficientNet B5, known for its exceptional performance in image classification tasks, has been tailored to excel in the challenging domain of medical image analysis. Through its ability to extract intricate features and patterns from MRI images, it empowers clinicians and researchers to identify subtle neuroanatomical changes associated with brain tumor. The algorithm\'s capacity to learn and adapt from large datasets ensures its capability to discern even nuanced abnormalities, thus enabling early diagnosis when treatment options are most effective. Moreover, the implementation of EfficientNet B5 in brain tumor detection offers advantages beyond improved accuracy. It streamlines the diagnostic process, reducing the time and effort required for manual image interpretation. This, in turn, can lead to more timely interventions, better patient outcomes, and potentially lower healthcare costs. However, it is important to acknowledge that while the results are promising, there are still challenges to address. The algorithm\'s robustness and generalizability across diverse patient populations, as well as its ability to handle variations in MRI acquisition protocols, require further research and validation. Additionally, ethical considerations, data privacy, and regulatory compliance are paramount when deploying such technology in clinical settings. In summary, the application of the EfficientNet B5 algorithm in brain tumor detection from MRI images showcases the potential of artificial intelligence to revolutionize healthcare by enhancing the accuracy and efficiency of diagnosis. With ongoing research and development, this technology holds great promise for improving the lives of individuals affected by brain tumor and advancing our understanding of this debilitating condition. As we continue to refine and expand upon these methodologies, we move closer to a future where early brain tumor detection becomes a routine and accessible part of healthcare, ultimately leading to improved patient care and outcomes.

References

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Copyright

Copyright © 2025 Bobby K Simon, A. Likhith Kumar , K. Karthika Sri, T. Chandrika, E. Pranay. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.