Added Malaria Cell Classification Using CNN and Transfer Learning

[IkkiOcean]

Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

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Issue Title

Malaria Cell Classification Using CNN and Transfer Learning

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Info about the Related Issue

What's the goal of the project?
The goal of this project is to automate the classification of malaria-infected and uninfected cells using machine learning and deep learning models. By utilizing CNNs and transfer learning, the project aims to increase diagnostic accuracy and reduce the time needed to identify parasitized cells from microscope images.

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Name

Vivek Prakash

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GitHub ID

https://github.com/IkkiOcean

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Email ID

vivekprakash.st@gmail.com

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Identify Yourself

Participating in GSSOC-EXT

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Closes

**Closes: #926 **

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Describe the Add-ons or Changes You've Made

Give a clear description of what you have added or modified.
This PR introduces the following changes:

1. Dataset Integration: Added preprocessed malaria cell images categorized as parasitized and uninfected, including data augmentation.
2. Models:
   • MLP: Implemented as a baseline with ~65% accuracy.
   • CNN: Custom architecture achieving 96% accuracy on training and 94% on testing.
   • CNN with Regularization: Regularized CNN with similar accuracy to base CNN.
   • Transfer Learning (VGG19): Fine-tuned VGG19 model trained for 1 epoch to leverage pre-trained weights.
3. Documentation: Added README with project overview, dataset details, model architectures, and performance summaries.

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Type of Change

Select the type of change:

• 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

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How Has This Been Tested?

Describe how your changes have been tested.
The models were trained and tested on the malaria cell dataset, with accuracies recorded on training and testing sets to evaluate performance. The models were also validated with data augmentation to check for overfitting and generalization. Model performance was compared through metrics such as accuracy on both train and test datasets.

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Checklist

Please confirm the following:

• 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.

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[UTSAVS26]

@IkkiOcean explain about the dataset too about its features and parameters in the dataset readme file.

[IkkiOcean]

@IkkiOcean explain about the dataset too about its features and parameters in the dataset readme file.

okay sure.

[IkkiOcean]

@UTSAVS26 done with the required changes, please merge it soon