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Digital Image Processing for Medical Applications

During my first's year challenge project (Learning by doing), we worked on a project: "image processing for medical diagnosis" proposed by the company Zen Networks, in which we developed an intelligent system that allows the analysis of medical images for the detection of anomalies such as tumors; this to combat the problem of non-detection or late detection of anomalies in patients. As a result of this project, we have succeeded in developing a high-performance and reliable program for detecting brain cancer in MRI based on a specified treatment of a brain MRI database using mainly deep learning algorithms that we have deployed to refine the classification of images. We have also developed a program able to detect COVID-19 in an X-ray image of the lungs. Using machine learning techniques, we were able to make medical diagnoses and differentiate between healthy patients and those with abnormalities.

As part of my formation in Ecole Centrale Casablanca, we were led to work in groups of four on this project, supervised by Mr Mohamed Hassan Khalili (professor-researcher at Ecole Centrale Casabalanca).

  • Artificial Intelligence
  • Image processing
  • Deep Learning
  • CNN : VGG16, ResNet 50, InceptionV3, MobileNetV2
  • Python librairies : OpenCV, Tensorflow, Keras, Scikit-learn, matplotlib, numpy, etc.


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Challenge Project In Ecole Centrale Casablanca - Learning by doing

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