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https://github.com/somenath203/malaria-cell-image-classification
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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
Last synced: 10 days ago
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- Host: GitHub
- URL: https://github.com/somenath203/malaria-cell-image-classification
- Owner: somenath203
- Created: 2024-04-01T02:58:06.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-09T18:44:28.000Z (about 2 months ago)
- Last Synced: 2024-12-09T19:36:49.929Z (about 2 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: 1.05 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Malaria Cell Image Classification using TensorFlow
## Demo video of the application
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.
## Making a Successful Payment with Razorpay
To complete your payment through Razorpay, simply follow these steps:
1. **Choose the "UPI" payment method**
2. **Provide the UPI ID**
- Enter: `success@razorpay`.
3. **Payment Confirmation**
- Your payment will be processed successfully.
## 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.