Moiz Hassan · imagine-talent

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Academic

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CGPA

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Year

2021

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Career

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Skills

Keras, Tensorflow, Raspberry Pi, HTML 5, CSS3, JavaScript, Machine Learning, 3D UNET CNN

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.

Brain Stroke Diagnosis using AI 
Brain stroke is suffered by 15 million people world-wide causing death and disability to around 10 
million people due to lack of timely treatment. Treatment using manual CT scan inspection is a 
timely process. Our Machine Learning algorithm using 3D UNET CNN deployed on a raspberry Pi 
and connected with hospital database and website will give the diagnosis and segmentation results 
in fraction of the time and cost with accuracy.  
This is a low cost unit which can be implemented in hospitals in Pakistan preventing death and 
disabilities suffered by Brain Stroke patients. 
 
 
 
 
 
 
 
 
 
 
 
Technology Used: 
Keras with Tensorflow Backend 
Raspberry Pi,HTML 5, CSS3, JavaScript 
Supervisor Name: 
Dr. Farhan Khalid 
Group Members:   
Moiz Hassan (i17-0414) 
Omair Hassan (i17-0444)

AI enrichment

Moiz Hassan is a student who worked on a brain stroke diagnosis project using a 3D UNET CNN model deployed on a Raspberry Pi. The project utilized Keras with a TensorFlow backend and web technologies to provide low-cost, timely medical diagnostics.

Skills (AI)

["Machine Learning", "Deep Learning", "3D UNET CNN", "Keras", "TensorFlow", "Raspberry Pi", "HTML5", "CSS3", "JavaScript"]

Status: ai_done

Provenance

Source file: Graduate Directory FAST School of Engineering - 2021 (Final) (1).pdf
From job #21 page 74
Created: 1778170055