Pinned Repositories
Actualmed-COVID-chestxray-dataset
Actualmed COVID-19 Chest X-ray Dataset Initiative
COVID-19
Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-Ray Images
COVID-19-Detection
COVID-19 Detection Using Chest X-Ray Images and Deep Convolutional Neural Networks
COVID-19-DetectionV2
COVID-19 Detection Using Chest X-Ray Images and Deep Convolutional Neural Networks
covid-chestxray-dataset
We are building an open database of COVID-19 cases with chest X-ray or CT images.
COVID-Net
COVID-Net model for COVID-19 detection on COVIDx dataset
COVID19_imaging_AI_paper_list
COVID-19 imaging-based AI paper collection
Figure1-COVID-chestxray-dataset
Figure 1 COVID-19 Chest X-ray Dataset Initiative
One-Stop-for-COVID-19-Infection-and-Lung-Segmentation-plus-Classification
✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation
Pneumonia-Diagnosis-using-XRays-96-percent-Recall
BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.
drcerenkaya's Repositories
drcerenkaya/COVID-19-Detection
COVID-19 Detection Using Chest X-Ray Images and Deep Convolutional Neural Networks
drcerenkaya/COVID-19-DetectionV2
COVID-19 Detection Using Chest X-Ray Images and Deep Convolutional Neural Networks
drcerenkaya/Actualmed-COVID-chestxray-dataset
Actualmed COVID-19 Chest X-ray Dataset Initiative
drcerenkaya/covid-chestxray-dataset
We are building an open database of COVID-19 cases with chest X-ray or CT images.
drcerenkaya/COVID-Net
COVID-Net model for COVID-19 detection on COVIDx dataset
drcerenkaya/COVID19_imaging_AI_paper_list
COVID-19 imaging-based AI paper collection
drcerenkaya/COVID-19
Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-Ray Images
drcerenkaya/Figure1-COVID-chestxray-dataset
Figure 1 COVID-19 Chest X-ray Dataset Initiative
drcerenkaya/One-Stop-for-COVID-19-Infection-and-Lung-Segmentation-plus-Classification
✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation
drcerenkaya/Pneumonia-Diagnosis-using-XRays-96-percent-Recall
BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.