Ahmed's repositories
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.
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.
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.
Spam-and-Ham-text-classifier
In this project we are using LSTM to classify texts as spam or ham.
Stable-Diffusion
In this project we will train stable diffusion model on CIFAR10 dataset and then try to generate images form ten different classes.
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.
Simple-Computer-Vision-Codes-in-Python
In this you will find simple and easy Computer Vision Codes in Python
Book-Recommendation-System-using-KNN
In this project we used a k-nearest neighbors algorithm (KNN) to recommend a book based on your previous book prefrecnces.
Cat-and-Dog-Classifier
In this project we will classify Dog and Cat images using Convolution Neural Network (CNN)
Cost-Prediction
In this project we will predict the cost required for a patient depending on his/her health conditions.
Linear-Regression
Linear Regression using Matlab on a Kaggle dataset.
Twitter-Sentiment-Analysis-using-RNN-LSTM
In this Project we will train our RNN model by giving it different tweets and then predict the sentiments of the tweets.