{"id":10030,"date":"2025-05-12T15:09:59","date_gmt":"2025-05-12T19:09:59","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?page_id=10030"},"modified":"2025-12-12T02:45:41","modified_gmt":"2025-12-12T07:45:41","slug":"ayman-husain","status":"publish","type":"page","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/students\/2024-2\/cs488\/ayman-husain\/","title":{"rendered":"Ayman Husain"},"content":{"rendered":"\n

About Me<\/h2>\n\n\n\n

Hey there! My name is Ayman Husain and I am an international junior at Earlham College, currently working towards a Data Science and Business Management double major.<\/p>\n\n\n\n

Abstract<\/h2>\n\n\n\n

This project proposes the development of a supervised deep learning model to detect tuberculosis from chest X-ray images. It does so by classifying the images as either TB-positive or TB-negative by using the feature extractor to highlight key outliers in an image. In this project, we are essentially building three convolutional neural network (CNN) based models. One of these models we have built from scratch, and the other two models are pre-trained models specialized in medical image classification called Densenet and Resnet. Finally, we will be conducting a comparative analysis on how our model compares to the transfer learning-based model through several performance metrics detailed below.<\/p>\n\n\n\n

Graphical Abstract Diagram<\/h2>\n\n\n\n
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Data Architecture Diagram<\/h2>\n\n\n\n
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Project Poster<\/h2>\n\n\n\n
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Technical Project Report<\/h2>\n\n\n\n
Technical_Report_Final_DS488<\/a>Download<\/a><\/div>\n\n\n\n

Project Demonstration<\/h2>\n\n\n\n