Advanced Brain Tumor Detection in MRI Images using Deep Learning

Advanced Brain Tumor Detection in MRI Images using Deep Learning

IEEE BASE PAPER TITLE:

A Multimodal Adaptive Inter-Region Attention-Guided Network for Brain Tumor Classification

IEEE BASE PAPER ABSTRACT:

Accurate brain tumor classification is critical for ensuring timely and effective medical interventions. In recent years, artificial intelligence (AI)-driven diagnostic systems have emerged as transformative tools that optimize the classification process and enable rapid, objective decision-making. However, existing methods often suffer from limitations such as the loss of high-frequency details during multimodal preprocessing, inadequate cross-modal feature alignment, and insufficient focus on shared tumor regions within 3D architectures. To address these challenges, this study introduces a novel AI-based framework for advanced brain tumor classification. Specifically, we propose a multimodal magnetic resonance imaging (MRI) architecture that integrates Diffusion-Weighted MRI (DW-MRI) and T2-weighted MRI (T2-MRI) modalities, uniquely combining them in a dual-branch 3D neural architecture with advanced preprocessing and attention mechanisms. The preprocessing pipeline employs a learnable High-Frequency Information Retention (HFIR) technique to resize T2-MRI images, maintaining consistent spatial dimensions across modalities while preserving essential image details. The architecture utilizes dual-branch 3D convolutional neural networks (CNN) for modality-specific feature extraction, enhanced by a novel Adaptive Region Attention (ARA) module that dynamically aligns and emphasizes highly informative regions shared across modalities, providing deeper and more consistent insights into tumor characteristics. Rigorous evaluation on a dataset of brain MRI scans including three tumor classes demonstrates that the proposed framework achieves overall accuracy, sensitivity, and specificity of 92.86%, 80.00%, and 94.12%, respectively. Statistical analyses using bootstrap-resampled F1-scores confirm significant outperformance over other state-of-the-art models, underscoring its robust and interpretable potential for precise brain tumor diagnosis.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

YOLOv8 Architecture.

OUR PROPOSED PROJECT ABSTRACT:

Brain tumor detection from magnetic resonance imaging (MRI) plays a critical role in early diagnosis, treatment planning, and clinical decision-making. Manual analysis of MRI scans is time-consuming and highly dependent on expert radiologists, motivating the need for automated, accurate, and real-time diagnostic solutions. With recent advancements in deep learning, object detection architectures have shown significant potential in extracting complex spatial features from medical images and improving diagnostic reliability.

This project presents an Advanced Bran Tumor Detection in MRI Images using Deep Learning, designed to address the challenges of accuracy, speed, and usability in tumor identification. The system is developed using Python for model implementation, Flask as the web framework, and HTML, CSS, and JavaScript for an interactive and user-friendly frontend. The proposed solution leverages the YOLOv8 architecture, which enables efficient object detection by simultaneously localizing and classifying tumor regions within MRI images.

The model is trained and evaluated on a well-structured dataset consisting of 10,495 training images, 600 validation images, and 300 testing images, categorized into two classes: tumor and no tumor. Experimental results demonstrate strong detection performance, achieving a mAP@50 of 95.8%, indicating high accuracy and robustness in distinguishing tumor-affected regions from normal brain scans.

To enhance practical applicability, the developed system supports two operational modes. The Image Mode allows users to upload MRI images and obtain instant tumor detection results, while the Live Mode utilizes a web camera to perform real-time detection, showcasing the model’s capability for continuous and dynamic analysis. In addition to prediction outputs, the system provides a comprehensive performance analysis module, including visual graphs such as precision–recall curves, confidence-based metrics, and training–validation trends, enabling transparent evaluation of model behavior.

Overall, this project demonstrates an effective and scalable deep learning-based solution for brain tumor detection, combining high detection accuracy, real-time inference, and intuitive visualization, thereby offering a reliable decision-support tool for medical imaging applications.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 20 GB.
• Monitor : 15’’ LED.
• Input Devices : Keyboard, Mouse.
• Ram : 8 GB.
• Camera : Webcam

SOFTWARE REQUIREMENTS: 

• Operating System : Windows 10 / 11.
• Coding Language : Python 3.12.0.
• Web Framework : Flask.
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

Ibrahim Abdelhaliem; Jose Dixon; Abeer Abdelhamid; Gehad A. Saleh; Fahmi Khalifa, “A Multimodal Adaptive Inter-Region Attention-Guided Network for Brain Tumor Classification”, IEEE Access, Volume 13, 2025.

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Frequently Asked Questions (FAQ’s) and Answers

1. What is the objective of this project?

The objective of this project is to develop an automated system that detects brain tumors in MRI images using deep learning techniques. The system aims to provide accurate tumor localization and classification while supporting both image-based and real-time detection through a web-based platform.

2. Which deep learning model is used in this project?

The project uses the YOLOv8 (You Only Look Once version 8) architecture. YOLOv8 is a single-stage object detection model that enables simultaneous tumor localization and classification with high accuracy and fast inference.

3. What dataset is used for training and testing the model?

The model is trained and evaluated on a dataset consisting of 10,495 training images, 600 validation images, and 300 testing images, with two classes: tumor and no tumor. The dataset is designed to support binary classification and object detection tasks.

4. What performance metrics are achieved by the system?

The proposed system achieves a high detection performance with an mAP@50 (Bounding Box) of 95.8%. Additional evaluation metrics such as precision, recall, and F1-score are analyzed using graphical performance curves.

5. What are the operational modes supported by the system?

The system supports two operational modes: • Image Mode: Users can upload MRI images for tumor detection. • Live Mode: The system performs real-time detection using a web camera.

6. What technologies are used to develop the system?

The system is developed using Python for backend processing, Flask as the web framework, and HTML, CSS, and JavaScript for frontend development. Supporting libraries include OpenCV, Ultralytics YOLOv8, NumPy, and Pillow.

7. Can the system detect tumors in real time?

Yes, the system supports real-time tumor detection in Live Mode using a web camera. The YOLOv8 architecture enables fast inference, allowing continuous frame-by-frame analysis.

8. How are the results visualized in the system?

Detection results are visualized by highlighting tumor regions in MRI images. The system also provides performance analysis using graphs such as precision–recall curves, confidence-based metrics, F1–confidence curves, and training–validation performance plots.

9. Is the system user-friendly for non-technical users?

Yes, the system features a web-based interface that allows users to easily upload images, view detection results, and analyze performance without requiring technical expertise.

10. What makes this project different from traditional methods?

Unlike traditional manual analysis, this project automates tumor detection using deep learning, supports real-time processing, and provides comprehensive performance visualization through an interactive web interface.