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Malaria Cell Segmentation from Microscopic Blood Smear Images Using Machine-Learning & Deep-Learning (CSE299-Junior Design Course)
Malaria Cell Segmentation from Microscopic Blood Smear Images Using Machine-Learning & Deep-Learning (CSE299-Junior Design Course)
(To assist medical field a step forward in disease detection using deep learning approaches)
Dataset: Broad Bio-image Benchmark Collection. These images were contributed by Jane Hung of MIT and the Broad Institute in Cambridge, MA.There is also a Github repository that lists malaria parasite imaging datasets (blood smears) .
Preprocessing: Data annotation and augmentation
Models Tried: CNN, Pre-trained (Yolo V3), TensorFlow Object Detection API to fine-tune and train a faster RCNN.
Link:https://github.com/Anaskaysar/Malaria-Cell-Segmentation-Using-Machine-Learning-and-Deep-Learning
Covid-19 Detection Using Deep Learning (CSE 465- Pattern Recognition and Neural Network)
Covid-19 Detection Using Deep Learning (CSE 465- Pattern Recognition and Neural Network)
(Used Convolutional Neural Networks (CNN) for image recognition and classification to predict COVID-19)
Worked with a dataset containing a large amount of images of Chest X-Rays.
Collected images from multiple resources and compiled them and made our own dataset.
It was a group project consisting of three members. Each of us worked individually for couple of models
Build CNN model from scratch and also used pertained models (ex. ANN, VGG-16, DarkNet, Dim Reduction)
Link: https://github.com/Anaskaysar/COVID-19-Detection-From-X-Ray-Images
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