<div class="sc-jegwdG lhLRCf"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-davvxH eCVTlP"><div class="sc-jCNfQM ikRdXB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-gVIFzB gQKGyV"><p>🦴 <strong>Bone Fracture X-ray Dataset: Simple vs. Comminuted Fractures</strong> 🦴</p>

<p>🚀 Dive into a comprehensive dataset of X-ray images specifically curated for <strong>bone fracture classification</strong>, distinguishing between <strong>simple</strong> and <strong>comminuted</strong> fractures! This dataset is an invaluable resource for researchers and developers aiming to build and evaluate machine learning models in medical imaging.</p>

<p>🧑‍🔬 <strong>Contributors:</strong> Fahim Faisal Talha Talha, Md Soroar Jahan, Mayen Uddin Mojumdar<br>

📅 <strong>Published:</strong> December 3, 2024<br>

<strong>Version:</strong> v3<br>

🔗 <strong>DOI:</strong> 10.17632/vg95gvhj3y.3</p>

<h2>🔍 Dataset Overview</h2>

<p>This dataset is meticulously organized to facilitate the development of robust models for <strong>image classification</strong>, <strong>segmentation</strong>, and <strong>fracture type recognition</strong>. It comprises high-quality X-ray images along with a substantial set of <strong>augmented images</strong>, ensuring a diverse and challenging training environment.</p>

<h2>🦴 Fracture Categories</h2>

<ol>

<li><p><strong>Simple Fracture:</strong></p>

<ul>

<li><strong>Source:</strong> Exclusively sourced from hospital records for high clinical relevance.</li>

<li><strong>Original Images:</strong> <strong>1,211</strong> images.</li>

<li><strong>Augmented Images:</strong> <strong>6,311</strong> images.</li>

<li><strong>Total Images:</strong> <strong>7,522</strong> images.</li></ul></li>

<li><p><strong>Comminuted Fracture:</strong></p>

<ul>

<li><strong>Source:</strong> A blend of hospital-sourced images and web-sourced images (approximately one-tenth from web pages) to represent a broader range of cases.</li>

<li><strong>Original Images:</strong> <strong>1,173</strong> images.</li>

<li><strong>Augmented Images:</strong> <strong>7,366</strong> images.</li>

<li><strong>Total Images:</strong> <strong>8,539</strong> images.</li></ul></li>

</ol>

<h2>✨ Key Features</h2>

<ul>

<li><strong>Number of Original Images:</strong> <strong>2,384</strong></li>

<li><strong>Number of Augmented Images:</strong> <strong>13,677</strong></li>

<li><strong>Total Dataset Size:</strong> <strong>16,061</strong> images (<strong>Original</strong> + <strong>Augmented</strong>)</li>

<li><strong>File Format:</strong> JPG</li>

</ul>

<h2>🔄 Augmentation Techniques</h2>

<p>To enhance the diversity and robustness of the dataset, the following augmentation techniques were applied:</p>

<ul>

<li><strong>Zoom:</strong> Randomized scaling to simulate different viewing distances.</li>

<li><strong>Rotation:</strong> Rotations up to ±30° to account for variations in image orientation.</li>

<li><strong>Brightness and Contrast Adjustments:</strong> Random adjustments within the 80%–120% range to mimic varying lighting conditions.</li>

<li><strong>Flips:</strong> Both vertical and horizontal flips to introduce symmetry variations.</li>

</ul>

<h2>🗓️ Update Details (Version 3)</h2>

<ul>

<li><strong>Original Submission Date:</strong> November 15, 2024</li>

<li><strong>Update Date:</strong> December 2, 2024</li>

</ul>

<p><strong>Changes in This Version:</strong></p>

<ul>

<li><strong>Expanded Dataset Size:</strong> Increased significantly from 6,798 to <strong>16,061</strong> total images.</li>

<li><strong>Augmented Images:</strong><ul>

<li><strong>Simple Fracture:</strong> Increased from 3,280 to <strong>6,311</strong> images.</li>

<li><strong>Comminuted Fracture:</strong> Increased from 2,900 to <strong>7,366</strong> images.</li></ul></li>

<li><strong>Augmentation Enhancements:</strong> Additional transformations were implemented to ensure a wider variety of realistic training images.</li>

</ul>

<h2>🎯 Applications</h2>

<p>This dataset holds immense potential for various applications, including:</p>

<ul>

<li><strong>Medical Imaging:</strong> Train and evaluate advanced models for accurate fracture classification and identification.</li>

<li><strong>Healthcare Technology:</strong> Support the development of innovative diagnostic tools and mobile applications for real-time fracture detection, potentially improving patient care.</li>

<li><strong>Medical Research:</strong> Facilitate a deeper understanding of fracture patterns and their visual characteristics.</li>

</ul>

<h2>🏥 Dataset Collection</h2>

<ul>

<li><strong>Simple Fracture Images:</strong> Sourced directly from hospitals to guarantee clinical accuracy and relevance.</li>

<li><strong>Comminuted Fracture Images:</strong> Obtained through a combination of hospital records and diverse web sources to represent a wide spectrum of imaging scenarios.</li>

</ul>

<h2>💡 Realistic Scenarios</h2>

<p>The dataset is designed to simulate real-world clinical environments, featuring variations in lighting conditions, image orientations, and overall imaging environments. This approach ensures that models trained on this data are robust and generalizable.</p>

<h2>📜 Citation</h2>

<p>Talha, Fahim Faisal Talha; Jahan, Md Soroar; Mojumdar, Mayen Uddin (2024), “Bone Fracture X-ray Dataset: Simple vs. Comminuted Fractures”, Mendeley Data, V3, doi: 10.17632/vg95gvhj3y.3</p>

<p>🙏 If you find this dataset valuable for your research, please <strong>upvote</strong>! 👍 Your support is greatly appreciated! 😊</p></div></div></div>