Zoya Sumbhul
FAST
· 2022
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I18-0721
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Year
2022
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Career
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Skills
Python, React, TensorFlow, Visual Studio
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Machine Learning and Imaging processing based Lung diseases detection application (MILDDA) We made an application that detects lung diseases such as COVID-19, pneumonia, lung cancer and, tuberculosis using chest x-rays. The radiologists can upload medical images. The images are then processed and cleaned first, then they are passed through an optimized Visual Geometry Group (VGG) which then classifies the images into diseases. The probability of a given disease is also shown to give an accuracy measure. This system helps point out the critical cases in a particular class/disease. This system also makes the detection of Covid-19 faster. This system be there to help the doctors and radiologists as they will make the final decision for the patient. This system also detects the severity of each disease and classifies it into mild, moderate and severe. Features include: -Detection of lung disease such as lung cancer, pneumonia, tuberculosis, COVID-19. -Detection of the severity of each disease classified into mild, moderate and severe. -Upload reports, prescription and patient history. -View reports, prescription and patient history. Technology Used: Python, React, TensorfLow, 4.6, Visual Studio Supervisor Name: Mr. Bilal Khalid Dar Group Members: Zoya Sumbhul (I18 - 0721)
AI enrichment
Zoya Sumbhul is a student who developed a machine learning application for detecting lung diseases using chest X-rays and an optimized VGG model. The project involved processing medical images and classifying diseases such as COVID-19 and pneumonia, integrated with a React frontend.
Skills (AI)
["Python", "React", "TensorFlow", "Machine Learning", "Image Processing", "VGG", "Visual Studio"]
Status: ai_done
Provenance
Source file: Graduate Directory FAST School of Computing 2022 Final Version (07-06-2022).pdfFrom job #25 page 238
Created: 1778170933