[Project Addition] : Chest X-ray - Tuberculosis Disease Prediction

[aaradhyasinghgaur]

Pull Request for DL-Simplified 💡

Issue Title : Chest X-ray - Tuberculosis Disease Prediction

• Info about the related issue (Aim of the project) : To predict and classify between Normal x-ray and tuberculosis diseases using chest X-ray images, a convolutional neural network (CNN) can be employed. This deep learning model will process the X-ray images to identify characteristic features and patterns indicative of each disease. The trained model will output a probability score for each disease, allowing for accurate and efficient diagnosis based on the visual data contained in the chest X-rays.
• Name: Aaradhya Singh
• GitHub ID: https://github.com/aaradhyasinghgaur
• Email ID: [email protected]
• Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) GSSOC-2024 Contributor

Closes: #755

Describe the add-ons or changes you've made 📃

1. Data Preparation: Collect and preprocess a balanced dataset of real and fake face images, including normalization, resizing, and augmentation.
2. Base Model Selection: EfficientNetB0,VGG16 , Xception , InceptionV3 like 5 different models excluding its top layers, to leverage its learned features.
3. Model Construction: Add custom layers on top of the base model for binary classification, compiling with appropriate loss and metrics.
4. Initial Training: Train the model with the base layers frozen to only update the new layers.
5. Fine-Tuning: Unfreeze some or all of the base model layers and continue training with a lower learning rate to fine-tune the entire network.
   6.) EDA analysis.
   7.) Comaprioson using performance matrices such as accuracy scores , confusion matrix etc.

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: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• 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.

[github-actions]

Our team will soon review your PR. Thanks @aaradhyasinghgaur :)

[aaradhyasinghgaur]

@abhisheks008 kindly review my pr and assign me new issue if possible . Thank you

[abhisheks008]

Approved @aaradhyasinghgaur