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  1. index.html +8 -8
index.html CHANGED
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  <link rel="preconnect" href="https://fonts.googleapis.com" />
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  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
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  <link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600;700&display=swap" rel="stylesheet" />
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- <title><strong>Body Part Classification</strong></title>
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  </head>
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  <body>
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  <div class="container">
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  <div class="grid-container">
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  <h3 class="overview-heading"><span class="vl">Overview</span></h3>
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  <p class="overview-content">
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- 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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  </p>
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  </div>
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  <div class="grid-container">
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  <h3 class="overview-heading"><span class="vl">Dataset</span></h3>
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  <div>
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  <p class="overview-content">
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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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  <br/>
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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.
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  </p>
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  <ul>
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- <li>Objective: Body Part Identification</li>
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  <li>Task: Classification</li>
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- <li>Modality: Grayscale Images</li>
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  </ul>
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  </div>
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  </div>
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  <h3 class="overview-heading"><span class="vl">Model Architecture</span></h3>
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  <div>
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  <p class="overview-content">
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- The model architecture of ResNet18 to train x-ray images for classifying body part.
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  </p>
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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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  </div>
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  </div>
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  </section>
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  <h3 class="overview-heading"><span class="vl">Demo</span></h3>
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- <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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  </div>
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  </body>
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  </html>
 
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  <link rel="preconnect" href="https://fonts.googleapis.com" />
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  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
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  <link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600;700&display=swap" rel="stylesheet" />
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+ <title><strong>HAM 1000 Classification</strong></title>
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  </head>
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  <body>
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  <div class="container">
 
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  <div class="grid-container">
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  <h3 class="overview-heading"><span class="vl">Overview</span></h3>
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  <p class="overview-content">
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+ 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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  </p>
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  </div>
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  <div class="grid-container">
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  <h3 class="overview-heading"><span class="vl">Dataset</span></h3>
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  <div>
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  <p class="overview-content">
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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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  <br/>
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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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  </p>
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  <ul>
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+ <li>Objective: HAM 1000 Identification</li>
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  <li>Task: Classification</li>
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+ <li>Modality: Colour Images</li>
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  </ul>
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  </div>
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  </div>
 
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  <h3 class="overview-heading"><span class="vl">Model Architecture</span></h3>
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  <div>
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  <p class="overview-content">
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+ The model architecture of ResNet18 to train images for classifying skin cancer part.
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  </p>
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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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  </div>
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  </div>
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  </section>
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  <h3 class="overview-heading"><span class="vl">Demo</span></h3>
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+ <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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  </div>
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  </body>
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  </html>