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MasterHead

Hi šŸ‘‹, I'm Ahmed Sana

A passionate AI Engineer | Machine Learning Enthusiast | Artificial Neural Network Expert

ahmedsana

  • šŸ”­ Iā€™m currently working on Image Regeneration

  • šŸŒ± Iā€™m currently learning Image Generation

  • šŸ’¬ Ask me about Neural Networks, Machine Learning, Computer Vision

  • šŸ“« How to reach me [email protected]

Languages and Tools:

arduino c git java matlab opencv pandas python scikit_learn tensorflow

ahmedsana

Ā ahmedsana

ahmedsana

Ahmed's Projects

ahmedsana icon ahmedsana

I am an Artificial Intelligence Engineer with expertise in Machine Learning and Computer Vision.

binary-class-brain-tumor-classification. icon binary-class-brain-tumor-classification.

In this we trained a model to detect if there is a tumor in the brain image given to the model. Meaning a model for binary class with an accuracy of above 90 for same and cross validation.

binary-class-brain-tumor-segmentation-using-unet icon binary-class-brain-tumor-segmentation-using-unet

We segmented the Brain tumor using Brats dataset and as we know it is in 3D format we used the slicing method in which we slice the images in 2D form according to its 3 axis and then giving the model for training then combining waits to segment brain tumor. We used UNET model for our segmentation.

cost-prediction icon cost-prediction

In this project we will predict the cost required for a patient depending on his/her health conditions.

drowsiness-detection icon drowsiness-detection

In this project we will train our model on open and close eyes dataset then use that with face recognition library to check if the driver is sleeping or not.

four-class-brain-tumor-segmentation. icon four-class-brain-tumor-segmentation.

We segmented the Brain tumor using Brats dataset and as we know it is in 3D format we used the slicing method in which we slice the images in 2D form according to its 3 axis and then giving the model for training then combining waits to segment brain tumor. We used UNET model for training our dataset.

stable-diffusion icon stable-diffusion

In this project we will train stable diffusion model on CIFAR10 dataset and then try to generate images form ten different classes.

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