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]
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
Module
Description
Image Upload
Uploads MRI/CT images in standard formats
Preprocessing
Normalizes and resizes images; enhances for robustness
Disease Prediction
Classifies images into 4 categories using CNN
Result Display
Shows diagnosis with confidence scores
Performance Evaluation
Computes accuracy, precision, recall, and F1-score
User Interface
Web app for easy interaction and report download
7. Results & Performance
Disease-wise Accuracy:
Disease Type
Model
Accuracy (%)
Precision (%)
Recall (%)
F1-score (%)
Brain Tumor
CNN
94.32
93.80
95.20
94.49
Brain Stroke
SVM
91.45
89.88
92.00
91.00
Alzheimer’s Disease
RNN
87.60
86.20
88.10
87.14
Model Comparison:
Model
Accuracy (%)
F1-Score (%)
Our CNN
93.4
91.9
VGG16
89.7
87.8
ResNet50
91.5
90.1
MobileNetV2
88.3
87.0
InceptionV3
90.6
88.8
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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