https://github.com/somenath203/malaria-cell-image-classification

Click below to checkout the website
https://github.com/somenath203/malaria-cell-image-classification

chakra-ui cnn ensemble-learning fastapi huggingface image-classification keras malaria-cell-classification nextjs razorpay render tensorflow transfer-learning vercel

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• Host: GitHub
• URL: https://github.com/somenath203/malaria-cell-image-classification
• Owner: somenath203
• Created: 2024-04-01T02:58:06.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-04-30T05:07:30.000Z (7 months ago)
• Last Synced: 2024-04-30T06:52:55.087Z (7 months ago)
• Topics: chakra-ui, cnn, ensemble-learning, fastapi, huggingface, image-classification, keras, malaria-cell-classification, nextjs, razorpay, render, tensorflow, transfer-learning, vercel
• Language: Jupyter Notebook
• Homepage: https://malaria-cell-classifier.vercel.app/
• Size: 1.04 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Malaria Cell Image Classification using TensorFlow

## Demo video of the application

![malariacellclassification](https://github.com/user-attachments/assets/3535bf28-373b-4560-b83c-dec7c7ee3780)

https://www.youtube.com/watch?v=Nx-q1x9u4Bw

## Introduction

This is a project which uses deep learning algorithm to detect malaria in cell images.

## Dataset used in this project

The dataset used in this project is taken from here: https://lhncbc.nlm.nih.gov/LHC-research/LHC-projects/image-processing/malaria-datasheet.html

## Models used in this project

1) Alexnet

2) InceptionV3

3) resnet101

4) MobileNetV3

5) Ensemble Learning Model based on InceptionV3 and MobileNetV3

**Out of the all the above models, MobileNetV3 proved to be the most effective one with a training accuracy of around 94.42% and testing accuracy of around 93.40%**

## About the web application of the deep learning model

The deep learning model of this project is connected with a frontend webapp created with the help of NextJS via FastAPI for real time prediction. The frontend of the project is deployed on Vercel and the backend of the project is deployed on HuggingFace Spaces.

## Regarding making successful payment with Razorpay

To complete a payment with Razorpay, follow these steps:

1. **Select the "Cards" option.**

2. **Enter the card number:** `4111 1111 1111 1111`.

3. **Input the card expiry date:** Use any date in the future. For example, if today's date is 08/24 (August 2024), you can use an expiry date like 05/28 (May 2028), where 08 and 05 are months, and 24 and 28 are years.

4. **Enter the CVV:** You can use a number like `123` or `111`.

5. **Provide the OTP:** Enter a random 7-digit number, such as `8392653`.

Once you’ve completed these steps, your payment will be processed. For more information, watch the youtube video.

## Links

1) Live Preview: 

https://malaria-cell-classifier.vercel.app/

2) Backend FastAPI link of the model: https://som11-malaria-cell-classification.hf.space/

3) Swagger documentation of the FastAPI of the deep learning model: https://som11-malaria-cell-classification.hf.space/docs

4) NodeJS API of the project: https://malaria-cell-detect-backend-nodejs.onrender.com/

## Warning

While the model of this project can detect malaria in cell images correctly, but in some cases, the model may misclassify or fail to detect malaria altogether, therefore, it is strongly advised not to rely solely on the output of this model.