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| | <title><strong>Body Part Classification</strong></title> |
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| | <h1 class="title"><strong> Body Part Classification</strong></h1> |
| | <h2 class="subtitle"><strong>Kalbe Digital Lab</strong></h2> |
| | <section class="overview"> |
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| | <h3 class="overview-heading"><span class="vl">Overview</span></h3> |
| | <p class="overview-content"> |
| | The Body Part Classification program serves the critical purpose of categorizing body parts from DICOM x-ray scans into five distinct classes: abdominal, adult chest, pediatric chest, spine, and others. This program trained using ResNet18 model. |
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| | <h3 class="overview-heading"><span class="vl">Dataset</span></h3> |
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| | The program has been meticulously trained on a robust and diverse dataset, specifically <a href="https://vindr.ai/datasets/bodypartxr" target="_blank">VinDrBodyPartXR Dataset.</a>. |
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| | This dataset is introduced by Vingroup of Big Data Institute which include 16,093 x-ray images that are collected and manually annotated. It is a highly valuable resource that has been instrumental in the training of our model. |
| | </p> |
| | <ul> |
| | <li>Objective: Body Part Identification</li> |
| | <li>Task: Classification</li> |
| | <li>Modality: Grayscale Images</li> |
| | </ul> |
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| | <h3 class="overview-heading"><span class="vl">Model Architecture</span></h3> |
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| | The model architecture of ResNet18 to train x-ray images for classifying body part. |
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| | <img class="content-image" src="file/figures/ResNet-18.png" alt="model-architecture" width="425" height="115" style="vertical-align:middle" /> |
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| | </section> |
| | <h3 class="overview-heading"><span class="vl">Demo</span></h3> |
| | <p class="overview-content">Please select or upload a body part x-ray scan image to see the capabilities of body part classification with this model</p> |
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