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Author: intsav

CV.html

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+<!-- ======= Project 11 ======= -->
+
+<div class = "project-6">
+  <div class="container project-container">
+    <div class="row justify-content-left d-flex flex-wrap align-items-center">
+      <div class="row justify-content-left">
+        <div class="col-12">
+          <div align="justify">
+             <h2 style="color:blue;"><b>Project 11: Advanced Optimization of Convolutional Neural Networks for Echocardiographic 
+               Image Analysis using Automated Hyperparameter Tuning</b></h2> <br>
+            
+        <h3><b>Introduction:</b><br><br>
+          In the field of medical imaging, particularly echocardiography, Convolutional Neural Networks (CNNs) have shown promising 
+          potential to enhance diagnostic processes through accurate image classification and segmentation. However, the optimal 
+          performance of CNN models is dependent on the precise tuning of hyperparameters, a task that is traditionally time-consuming 
+          and requires extensive experiments. This project proposes the use of automated hyperparameter optimization (HPO) techniques to 
+          streamline the optimization process, aiming to improve the efficiency and accuracy of CNN models for echocardiographic analysis. 
+          By leveraging advanced HPO methods and tools such as Keras Tuner and Optuna, the project seeks to eliminate the manual tuning bottleneck, 
+          fostering advancements in automated medical diagnostics.<br><br>
+         
+        <b>Aims:</b><br><br>
+        <ol>
+          <li>To review and assess the current landscape of CNN architectures and HPO techniques, focusing on their applications and 
+            performance in medical imaging, specifically echocardiography.</li>
+          <li>To implement and evaluate automated HPO strategies to enhance 
+            CNN models for echocardiographic view classification and image segmentation, comparing their effectiveness against traditional manual tuning methods.</li>
+        </ol><br>
+        <b>Significance:</b><br><br>
+         This project aims to significantly advance echocardiographic analysis by automating CNN model optimization, 
+         enhancing clinical diagnostics and making advanced AI tools more accessible to medical applications and researchers.
+          <br><br>
+        <b>Implementation and Datasets:</b><br><br>
+         You can implement automated hyperparameter tuning for the following tasks: <br><br>
+          <ol>
+          <li>Echocardiography View classification using the description and dataset in Project 3.</li>
+          <li>Left ventricular Segmentation in Echocardiography using the description and dataset in Project 6.</li>
+        </ol></h3>
+      <div align="center">
+          <br/>
+         <img class="img-fluid" src="assets\img\projects\Portfolio\CV\proj11.jpeg"   alt="Tuner Search Loop">
+
+      </div>
+
+    </div>
+  </div>
+</div>
+</div>
+<hr>
+</div>
+</div>
+
 
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