Brain Disease Detection Using AI

Abstract

This innovative project aims to transform the landscape of early medical diagnosis by leveraging advanced deep learning techniques for the accurate prediction of neurological disorders. The initial phase focused on developing a Convolutional Neural Network (CNN) model capable of classifying brain tumor, brain stroke, and Alzheimer’s disease using preprocessed medical imaging data. Building upon this foundation, the second phase enhances the model\'s diagnostic precision through improved data augmentation, model optimization, and inclusion of temporal analysis for progressive diseases. A robust evaluation module has been integrated, enabling the system to generate detailed diagnostic reports and probability-based predictions, supporting healthcare professionals in early and reliable detection. Additionally, a user-friendly interface has been developed for clinicians to upload brain scan images and receive real-time results. The project also explores integration with cloud platforms to ensure scalability and remote accessibility. This system has the potential to revolutionize early-stage neurological screening and assist in timely medical intervention, ultimately contributing to improved patient outcomes.

Introduction

1. Introduction

Early diagnosis of brain-related diseases like brain tumors, strokes, and Alzheimer’s disease is challenging due to:

• Late appearance of symptoms

• Manual and time-consuming image analysis

• Shortage of expert radiologists, especially in rural areas

• Fragmented diagnostic tools

To address these issues, an AI-powered Convolutional Neural Network (CNN) system was developed to automatically analyze brain scans and assist with accurate early diagnosis.

2. Related Work

Previous research highlights:

• CNNs and SVMs are effective for tumor detection but need large datasets [Smith et al., 2019]

• ANNs predict stroke risk using patient data [Gupta & Singh, 2020]

• LSTM networks forecast migraines using time-series data [Lee et al., 2021]

• Deep learning outperforms traditional classifiers like k-NN and Decision Trees for MRI analysis [Sharma & Verma, 2022]

3. Methodology

CNN Architecture:

• Layers: Convolution → ReLU → Fully Connected → Softmax

• Trained to classify: Brain Tumor, Stroke, Alzheimer’s, or Normal

Data Preprocessing & Augmentation:

• Normalization, resizing (e.g., 224×224)

• Techniques: rotation, zoom, flipping, shifting

Training Details:

• Loss Function: Categorical Cross-Entropy

• Optimizer: Adam

• Evaluation Metrics: Accuracy, Precision, Recall, F1-Score

4. System Deployment

• Web-based interface allows users (doctors/technicians) to:

  • Upload MRI/CT images

  • View disease predictions with confidence scores

  • Access hospital info, patient history

5. Objectives

• Enable early, automated diagnosis

• Assist in areas lacking medical specialists

• Reduce human error and diagnostic delays

• Ensure usability through a simple UI

• Maintain accuracy across limited or imbalanced datasets

6. Functional Requirements

7. Results & Performance

Disease-wise Accuracy:

Model Comparison:

8. Conclusion

The developed CNN-based diagnostic system:

• Achieves high accuracy in brain disease classification

• Is deployable in hospitals, especially in rural and underserved areas

• Supports faster, more accurate diagnosis, improving patient outcomes

• Outperforms traditional models like VGG16 and MobileNetV2

Conclusion

This research presents an AI-powered diagnostic system designed to assist in the early and accurate prediction of brain-related diseases such as brain tumors, strokes, and Alzheimer’s disease. By leveraging the power of Convolutional Neural Networks (CNNs), the proposed model demonstrates significant potential in reducing diagnostic time, minimizing human error, and improving healthcare outcomes. The system automates the analysis of medical imaging data, offering a fast and reliable second opinion for healthcare professionals. Through effective preprocessing, model training, and performance evaluation, the system proves to be a promising step toward integrating artificial intelligence into real-world clinical practices. Furthermore, its accessible design and potential for deployment in under-resourced areas make it a valuable contribution to smart and inclusive healthcare. In the future, this system can be expanded to include more neurological conditions and integrated with hospital systems for large-scale use

References

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Copyright

Copyright © 2025 Prof. H. R. Agashe, Saurabh Gite, Kunal Hande, Aditya Tile, Sakshi Warungase . 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.