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| | <title><strong>HAM10000 Classification</strong></title> |
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| | <h1 class="title"><strong>HAM10000 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 HAM10000 Skin Cancer Classification program serves the critical purpose of categorizing skin lesions into seven distinct classes: akk, bcc, bkl, df, mel, nv, and vasc. This program is trained using a 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://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000" target="_blank">Skin Cancer : HAM10000 Dataset.</a>. |
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| | The program has been meticulously trained on a diverse and extensive dataset, known as the HAM10000 dataset. This dataset consists of 10,015 dermatoscopic images of skin lesions, meticulously collected and manually annotated. It is a valuable resource that has played a pivotal role in training our skin cancer classification model. |
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| | <ul> |
| | <li>Objective: HAM 10000 Identification</li> |
| | <li>Task: Classification</li> |
| | <li>Modality: Colour 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 images for classifying skin cancer 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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| | <h3 class="overview-heading"><span class="vl">Demo</span></h3> |
| | <p class="overview-content">Please select or upload a skin cancer scan image to see the capabilities of skin part classification with this model</p> |
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