Skip to content

Latest commit

 

History

History
61 lines (35 loc) · 2.2 KB

readme.md

File metadata and controls

61 lines (35 loc) · 2.2 KB

Anemia Detection using Conjunctiva Images

Project Description

This project aims to detect anemia based on images of the conjunctiva (the inner part of the lower eyelid). Anemia is a condition characterized by a lack of healthy red blood cells, and it can often be detected by examining the color of the conjunctiva. Using a Convolutional Neural Network (CNN) implemented in TensorFlow, this model classifies images into anemic and non-anemic categories.

Dataset

The dataset used for this project is sourced from Kaggle and can be found here. The dataset consists of augmented images of the conjunctiva, labeled as anemic or non-anemic.

Model Architecture

The model is built using a Convolutional Neural Network (CNN) architecture, which includes:

  • Convolutional layers for feature extraction
  • Max-pooling layers for down-sampling
  • Fully connected layers for classification

Data augmentation techniques such as rotation, zoom, and flip were applied to enhance the training process and prevent overfitting.

Training

The model was trained using TensorFlow. Key details include:

  • Loss function: Binary Crossentropy
  • Optimizer: Adam
  • Metrics: Accuracy
  • Training epochs: 100
  • Validation split: 20%

Results

The model achieved an accuracy of 91% on the validation set. Below are some sample predictions and the accuracy plot over the epochs.

Sample Predictions:

Sample Predictions

Accuracy Plot:

Accuracy Plot

Loss Plot:

Loss Plot

Model Archtacteur :

model

The plot shows the training and validation accuracy over 100 epochs, demonstrating the model's learning progression.

Usage

To use this model, clone the repository and ensure you have the necessary dependencies installed:

git clone https://github.com/aymen-000/AIQuest_DATATHON.git
cd AIQuest_DATATHON
pip install -r requirements.txt