deadskull7 / 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.

Date Created 2018-05-29 (6 years ago)
Commits 47 (last one 6 years ago)
Stargazers 103 (0 this week)
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Forks 75
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RepositoryStats indexes 616,225 repositories, of these deadskull7/Pneumonia-Diagnosis-using-XRays-96-percent-Recall is ranked #295,537 (52nd percentile) for total stargazers, and #227,424 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #7,276/18,359.

deadskull7/Pneumonia-Diagnosis-using-XRays-96-percent-Recall is also tagged with popular topics, for these it's ranked: transfer-learning (#161/320),  healthcare (#87/167),  kaggle (#61/126)

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updated: 2024-12-29 @ 01:11pm, id: 135288671 / R_kgDOCBBXXw