Hajra Khan

Academic

Career

Verbatim text

SuperScan-Lesion Detection and Analysis in CT Scans 
 
 While computerized tomography (CT) may have been first-imaging tool to study human body, 
 but it has not yet been implemented into clinical decision-making process for diagnosis of 
 numerous diseases. However, the gravity  of the issue at hand enunciates that it takes an average of 
 about an hour for a radiologist to deduce astute conclusions, or to write a report of a CT scan. 
 Thereby, it takes about 24 hours for the report to get to the patient, regardless of the extremity of 
 conditions. In the meanwhile, the diagnostics industry suffers inefficient utilization of CT machines 
 due to long analysis processes of numerous similar CT images, that too manually.Our proposed 
 system accentuates to save the time and resources of radiologists by automatically detecting and 
 analyzing lesions in a CT Image. The proposed system has the following functional requirements: 
  Detect the exact location of the lesion in a patient’s body. 
  Analyze the type of lesion visible in the image. 
  Articulate special features, demonstrating the shape and surface structure of the lesion. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Technology Used: 
 Python, PyDicom, GDMC, ML & AI,  
 and Electron JS, Node JS,   
 Supervisor Name: 
 Dr. Muhammad Adnan Tariq 
 Group Members:   
 Hajra Khan (i16 - 0326)

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