Deep Learning Simplified is an Open-source repository, containing beginner to advance level deep learning projects for the contributors, who are willing to start their journey in Deep Learning. Devfolio URL, https://devfolio.co/projects/deep-learning-simplified-f013
Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) GSSOC-2024 contributor
Closes: #664
Describe the add-ons or changes you've made 📃
1.) Using 5 different available network architectures such as DenseNet121 , Xception , VGG16 etc. to do catagorical classification for chest X-ray images to detect COVID19 symptom .
2.) use data augmentation techniques to improve the accuarcy of models.
3.) Comparing performance and accuracy of models using accuracy score ,loss and accuracy graph , confusion matrix for better understanding.
4.) Perfroming EDA (data analysis) for dataset to understand the structure of data.
5.) Using README file for describing the work I've performed.
Type of change ☑️
What sort of change have you made:
[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Code style update (formatting, local variables)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
How Has This Been Tested? ⚙️
Describe how it has been tested
Describe how have you verified the changes made
Checklist: ☑️
[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[ ] Any dependent changes have been merged and published in downstream modules.
Pull Request for DL-Simplified 💡
Issue Title : [Project Addition] : Covid-19 X-Ray Image Classification
JWOC Participant
) GSSOC-2024 contributorCloses: #664
Describe the add-ons or changes you've made 📃
1.) Using 5 different available network architectures such as DenseNet121 , Xception , VGG16 etc. to do catagorical classification for chest X-ray images to detect COVID19 symptom . 2.) use data augmentation techniques to improve the accuarcy of models. 3.) Comparing performance and accuracy of models using accuracy score ,loss and accuracy graph , confusion matrix for better understanding. 4.) Perfroming EDA (data analysis) for dataset to understand the structure of data. 5.) Using README file for describing the work I've performed.
Type of change ☑️
What sort of change have you made:
How Has This Been Tested? ⚙️
Describe how it has been tested Describe how have you verified the changes made
Checklist: ☑️