Spaces:
Sleeping
Sleeping
File size: 2,678 Bytes
5514904 4f97622 5514904 d8fb0e2 5514904 e453a22 5514904 97047a1 5514904 97047a1 5514904 4f97622 5514904 97047a1 5514904 97047a1 5514904 ce33d42 5514904 97047a1 5514904 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
<!DOCTYPE html>
<html>
<head>
<link rel="stylesheet" href="file/style.css" />
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600;700&display=swap" rel="stylesheet" />
<title><strong>HAM10000 Classification</strong></title>
</head>
<body>
<div class="container">
<h1 class="title"><strong>HAM10000 Classification</strong></h1>
<h2 class="subtitle"><strong>Kalbe Digital Lab</strong></h2>
<section class="overview">
<div class="grid-container">
<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: Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanoma, Melanocytic Nevi, and Vascular Lesion. This program is trained using a ResNet18 model.
</p>
</div>
<div class="grid-container">
<h3 class="overview-heading"><span class="vl">Dataset</span></h3>
<div>
<p class="overview-content">
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>.
<br/>
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.
</p>
<ul>
<li>Objective: HAM 10000 Identification</li>
<li>Task: Classification</li>
<li>Modality: Colour Images</li>
</ul>
</div>
</div>
<div class="grid-container">
<h3 class="overview-heading"><span class="vl">Model Architecture</span></h3>
<div>
<p class="overview-content">
The model architecture of ResNet18 to train images for classifying skin cancer part.
</p>
<img class="content-image" src="file/figures/ResNet-18.png" alt="model-architecture" width="425" height="115" style="vertical-align:middle" />
</div>
</div>
</section>
<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>
</div>
</body>
</html> |