Merge pull request #414 from IkkiOcean/malaria

Malaria Cell Classification using CNN, and Transfer Learning (VGG19)

Neural Networks/malaria classification/dataset/readme.md

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+Link for the dataset : https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria

Neural Networks/malaria classification/readme.md

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+# Malaria Cell Classification
+
+This project involves classifying malaria-infected and uninfected cells using different machine learning and deep learning approaches. The primary goal is to automate the identification of parasitized and uninfected cells using microscopic images.
+
+## Overview
+
+Malaria is a critical disease caused by parasites, and detecting it in cells is a time-consuming process for healthcare workers. This project aims to automate the process by using machine learning and deep learning models to classify cells as parasitized or uninfected from microscope images.
+
+## Dataset
+
+- [Dataset](https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria)
+## Models
+
+### MLP
+- **Performance**: 
+  - Accuracy on training data: ~65%
+  - Accuracy on test data: ~65%
+
+  While the model performs consistently on both train and test sets, the accuracy is too low to depend on for classification.
+
+### CNN
+- **Performance**: 
+  - Accuracy on training data: 96%
+  - Accuracy on test data: 94%
+
+  The CNN model performs well with high accuracy on both training and test datasets.
+
+### CNN with Regularization
+- **Performance**: 
+  - Accuracy on training data: 96%
+  - Accuracy on test data: 94%
+
+  Adding regularization to the CNN model had no significant effect on the performance, resulting in similar accuracy to the base CNN model.
+
+### Hyperparameter Tuning
+- **Performance**: 
+  - Improved model performance, but hyperparameter tuning was time-consuming due to the large number of permutations tried. It was challenging to achieve faster training times with exhaustive tuning.
+
+### Transfer Learning (VGG19)
+- **Performance**: 
+  - Using transfer learning with the VGG19 architecture was applied for 1 epoch due to high computational cost.
+  - The model shows promise, but training for more epochs is needed for better performance.
+
+## Signature
+
+Vivek Prakash
+
+[Linkedin](https://www.linkedin.com/in/vivek-prakash-b46830283/)