Bone Fracture Detection using Deep Learning

Bone Fracture Detection using Deep Learning

IEEE BASE PAPER TITLE:

Enhancing Bone Fracture Classification in X-Ray Using Deep Learning Models

IEEE BASE PAPER ABSTRACT:

Millions of cases of bone fractures are reported every year, and accuracy in classification is crucial to help with proper management and treatment. The recently developed techniques of Machine Learning, particularly Deep Learning, have been effective in increasing diagnosis precision and efficiency. We utilized a diverse dataset comprising 10 different classes of fracture types captured in X-Ray images. This paper makes a comparison of different machine learning models on classifying bone fractures: VGG-16, VGG-16 with Random Forest, ResNet-50 with Support Vector Machine, and EfficientNetB0 with XGBoost. Model performances were evaluated with respect to parameters of precision, recalls, and F1-scores. According to results, VGG-16 and its variant ensemble with Random Forest outperformed with an accuracy of 0.95 when compared to others on every parameter for different classes of fractures. Results indicate that models based on VGG16 are quite effective for bone fracture classification.

PROJECT OUTPUT VIDEO:

ALGORITHM /MODEL USED:

YOLOv8.

OUR PROPOSED PROJECT ABSTRACT:

Bone fractures are among the most common musculoskeletal injuries, requiring timely and accurate diagnosis to ensure effective treatment. This project presents a robust web-based application for automated bone fracture detection using deep learning, designed to assist medical professionals in identifying fractures from X-ray images with high precision. The system is developed using Python for backend processing, with HTML, CSS, and JavaScript forming the interactive front end, and Flask serving as the web framework to integrate the components seamlessly.

The core of the application is powered by the YOLOv8 (You Only Look Once version 8) object detection algorithm, known for its real-time performance and high accuracy in image-based detection tasks. The model was trained and validated on a dataset comprising 3,316 training images and 399 validation images, covering seven distinct classes: Elbow Positive, Fingers Positive, Forearm Fracture, Humerus, Humerus Fracture, Shoulder Fracture, and Wrist Positive. The model achieved a commendable mean Average Precision (mAP50) of 86%, demonstrating its effectiveness in accurately localizing and classifying various types of bone fractures.

This project not only highlights the potential of deep learning in revolutionizing medical diagnostics but also provides an accessible, user-friendly platform that can be used in clinical settings for preliminary fracture screening. The successful implementation of this system underscores its potential to reduce diagnostic time and enhance the accuracy of fracture detection, particularly in regions with limited access to radiology expertise.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

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

SOFTWARE REQUIREMENTS: 

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

REFERENCE:

SPOORTHY TORNE, DASHARATHRAJ K. SHETTY, KRISHNAMOORTHI MAKKITHAYA, PRASIDDH HEGDE, MANU SUDHI, PHANI KUMAR PULLELA1, TAMIL ENIYAN T, RITESH KAMATH, STAISSY SALU, PRANAV BHAT, S. GIRISHA, AND P. S. PRIYA, “VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNetB0 With XGBoost-Enhancing Bone Fracture Classification in X-Ray Using Deep Learning Models”, IEEE Access, Volume: 13, 2025.

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Frequently Asked Questions & Answers:

1. What is the primary purpose of this bone fracture detection system?

The system is designed to automatically detect and localize various types of bone fractures in X-ray images using deep learning techniques. It helps medical professionals by providing fast, accurate identification of fractures, assisting in diagnosis and treatment planning.

2. What technology stack is used for developing this project?

The project uses Python for backend development, HTML, CSS, and JavaScript for the front-end interface, and Flask as the web framework. The deep learning model implemented is YOLOv8, known for its high-speed object detection capabilities.

3. How many types of fractures can the system detect?

1. Elbow Positive 2. Fingers Positive 3. Forearm Fracture 4. Humerus 5. Humerus Fracture 6. Shoulder Fracture 7. Wrist Positive

4. What is the accuracy of the model?

The system achieved a mean Average Precision (mAP50) of 86% during evaluation, indicating strong performance in detecting and localizing fractures.

5. What dataset was used to train and test the model?

The dataset consists of 3,316 training images and 399 validation images, containing labeled X-ray images for the seven specified fracture classes.

6. Does the system save the uploaded medical images?

No, the system processes the images temporarily for prediction and does not store them permanently, ensuring data privacy and security.

7. Can this system detect fractures in other body parts or from other imaging modalities?

Currently, the system is specialized for upper limb fractures based on X-ray images. However, it can be extended to include other body parts and imaging types (like CT or MRI) with additional training on relevant datasets.

8. Is the system compatible with all web browsers?

Yes, the system has been tested and is compatible with major browsers including Google Chrome, Mozilla Firefox, and Microsoft Edge.

9. How fast does the system process an uploaded image?

On average, the system provides detection results within 5 to 10 seconds of image upload, depending on the image size and server performance.

10. Is this system intended to replace radiologists?

No, the system is designed to assist, not replace, medical professionals. It serves as a diagnostic aid, offering quick initial assessments to help radiologists and clinicians make informed decisions more efficiently.