Windows Software Development Project -- 2
Budget: ₹600 – ₹1,500 INR
Project Title: AI-Based Brain Tumor Detection and Segmentation with Web Deployment
Abstract
Early detection and precise segmentation of brain tumors in MRI scans are critical challenges in modern healthcare. Brain tumors, whether benign or malignant, can severely affect a patient’s quality of life, and timely, accurate diagnosis is essential for improving treatment outcomes. Traditional diagnostic processes heavily rely on radiologists manually reviewing MRI images—a task that is time-consuming, subject to fatigue, and prone to human error. To address these limitations, this project proposes an AI-powered web platform that automates both tumor detection and segmentation from MRI scans using advanced deep learning techniques.
The system is designed with two main components:
Tumor Detection: A binary classification task that determines whether a brain tumor is present in the MRI scan.
Tumor Segmentation: A pixel-wise delineation of the tumor region, highlighting the exact location of the tumor within the brain.
Model Architectures
For detection, the system leverages pre-trained convolutional neural networks (CNNs) such as Xception and ResNet50V2, fine-tuned using transfer learning. These models were trained using techniques like dropout regularization, early stopping, and dynamic learning rate scheduling. In testing, the Xception model achieved an impressive 97.26% classification accuracy, outperforming other models in correctly identifying tumor presence.
For segmentation, a customized ResUNet architecture was developed. This model combines the strengths of residual learning and U-Net, allowing better feature propagation and training stability. The segmentation model achieved a Dice Coefficient of 89.4% and an Intersection over Union (IoU) of 81.3%, indicating strong overlap between predicted tumor regions and ground truth masks.
Web Deployment
The AI models were integrated into a Flask-based web application, enabling users to upload MRI scans and receive results in real-time. The web interface, developed separately using HTML, CSS, and JavaScript, offers two core functionalities:
Tumor Detection Mode – Displays whether a tumor is present.
Tumor Segmentation Mode – Returns the MRI image with the tumor region highlighted.
Key features of the platform include:
Support for images not present in the training dataset.
Real-time feedback and interactive visualization (zoom/pan) of results.
Backend model handling through Flask and AJAX for seamless user interaction.
Secure handling of uploaded files with validation and cleanup.
Dataset & Preprocessing
The training was conducted using a curated MRI dataset on Kaggle Notebooks. All images were resized to 256×256, normalized, and augmented with transformations like rotation, flipping, and brightness adjustments to enhance generalizability. To address class imbalance in the segmentation task, a custom Focal Tversky Loss function was used, which prioritizes hard-to-segment regions.
Current Limitations and Future Work
While the models perform well on the training and validation sets, current challenges include inconsistent predictions on external images not part of the dataset. This is likely due to domain shifts introduced by variations in MRI machine type, resolution, or acquisition protocols. Future work will focus on:
Incorporating additional, diverse datasets.
Improving generalization through domain adaptation techniques.
Supporting multi-class tumor classification.
Optimizing for edge deployment (e.g., mobile devices, embedded systems).
Enhancing model interpretability with explainable AI techniques.
Conclusion
This project successfully demonstrates the potential of AI in automating critical aspects of medical imaging analysis. By integrating cutting-edge deep learning architectures with a user-friendly web application, the system provides a powerful tool for brain tumor analysis. It bridges the gap between research and real-world clinical applications, offering speed, accuracy, and accessibility. As the system evolves, it holds promise not only in clinical diagnostics but also in education and research, highlighting the transformative role of AI in healthcare.
and it is urgent
Abstract
Early detection and precise segmentation of brain tumors in MRI scans are critical challenges in modern healthcare. Brain tumors, whether benign or malignant, can severely affect a patient’s quality of life, and timely, accurate diagnosis is essential for improving treatment outcomes. Traditional diagnostic processes heavily rely on radiologists manually reviewing MRI images—a task that is time-consuming, subject to fatigue, and prone to human error. To address these limitations, this project proposes an AI-powered web platform that automates both tumor detection and segmentation from MRI scans using advanced deep learning techniques.
The system is designed with two main components:
Tumor Detection: A binary classification task that determines whether a brain tumor is present in the MRI scan.
Tumor Segmentation: A pixel-wise delineation of the tumor region, highlighting the exact location of the tumor within the brain.
Model Architectures
For detection, the system leverages pre-trained convolutional neural networks (CNNs) such as Xception and ResNet50V2, fine-tuned using transfer learning. These models were trained using techniques like dropout regularization, early stopping, and dynamic learning rate scheduling. In testing, the Xception model achieved an impressive 97.26% classification accuracy, outperforming other models in correctly identifying tumor presence.
For segmentation, a customized ResUNet architecture was developed. This model combines the strengths of residual learning and U-Net, allowing better feature propagation and training stability. The segmentation model achieved a Dice Coefficient of 89.4% and an Intersection over Union (IoU) of 81.3%, indicating strong overlap between predicted tumor regions and ground truth masks.
Web Deployment
The AI models were integrated into a Flask-based web application, enabling users to upload MRI scans and receive results in real-time. The web interface, developed separately using HTML, CSS, and JavaScript, offers two core functionalities:
Tumor Detection Mode – Displays whether a tumor is present.
Tumor Segmentation Mode – Returns the MRI image with the tumor region highlighted.
Key features of the platform include:
Support for images not present in the training dataset.
Real-time feedback and interactive visualization (zoom/pan) of results.
Backend model handling through Flask and AJAX for seamless user interaction.
Secure handling of uploaded files with validation and cleanup.
Dataset & Preprocessing
The training was conducted using a curated MRI dataset on Kaggle Notebooks. All images were resized to 256×256, normalized, and augmented with transformations like rotation, flipping, and brightness adjustments to enhance generalizability. To address class imbalance in the segmentation task, a custom Focal Tversky Loss function was used, which prioritizes hard-to-segment regions.
Current Limitations and Future Work
While the models perform well on the training and validation sets, current challenges include inconsistent predictions on external images not part of the dataset. This is likely due to domain shifts introduced by variations in MRI machine type, resolution, or acquisition protocols. Future work will focus on:
Incorporating additional, diverse datasets.
Improving generalization through domain adaptation techniques.
Supporting multi-class tumor classification.
Optimizing for edge deployment (e.g., mobile devices, embedded systems).
Enhancing model interpretability with explainable AI techniques.
Conclusion
This project successfully demonstrates the potential of AI in automating critical aspects of medical imaging analysis. By integrating cutting-edge deep learning architectures with a user-friendly web application, the system provides a powerful tool for brain tumor analysis. It bridges the gap between research and real-world clinical applications, offering speed, accuracy, and accessibility. As the system evolves, it holds promise not only in clinical diagnostics but also in education and research, highlighting the transformative role of AI in healthcare.
and it is urgent