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 π
1.) Used 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.
Give a clear description of what have you added or modifications made
Type of change βοΈ
What sort of change have you made:
[ ] Bug fix (non-breaking change which fixes an issue)
[x] 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? βοΈ
Describe how it has been tested
Describe how have you verified the changes made
Checklist: βοΈ
[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.
[ ] 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.
Kindly add suitable label "Level-3" for the project as I have trained the dataset using 5 different models and compared their performance using several matrices.
Pull Request for DL-Simplified π‘
Issue Title : Covid-19 X-Ray Image Classification
JWOC Participant
) GSSOC-2024 contributorCloses: #664
Describe the add-ons or changes you've made π
1.) Used 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.
Give a clear description of what have you added or modifications made
Type of change βοΈ
What sort of change have you made:
How Has This Been Tested? βοΈ
Describe how it has been tested Describe how have you verified the changes made
Checklist: βοΈ
Kindly add suitable label "Level-3" for the project as I have trained the dataset using 5 different models and compared their performance using several matrices.