This project aims to develop a Blindness Detection system using machine learning and computer vision techniques. The system is designed to analyze medical images of the eye to identify signs of eye diseases and conditions, facilitating early diagnosis and treatment. Detecting eye diseases in their early stages is crucial for preventing vision loss and improving patient outcomes.
Introduction
Blindness detection is a critical application of machine learning and computer vision in the field of healthcare. This project demonstrates how to build, train, and evaluate a model capable of recognizing various eye diseases and conditions in medical images.
The Blindness Detection system was trained on a diverse and labeled dataset of medical images related to eye diseases and conditions. The dataset includes images of retinas, fundus photographs, and other relevant eye images. Data collaboration with medical professionals and institutions ensured the dataset's quality and clinical relevance.
Blindness Detection Using Machine Learning: Major Steps
1. Data Collection: Gather a labeled dataset of medical images (e.g., retinal images, fundus photographs) related to eye diseases and conditions, including images from healthy individuals for comparison. Collaborate with medical professionals and institutions to obtain high-quality and diverse data.
2. Data Preprocessing: Clean and preprocess medical images, including resizing, normalization, and artifact removal. Ensure images are properly labeled with information about the eye condition and disease stage.
3. Data Labeling: Accurately annotate each image with the corresponding eye disease or condition. Create labels for disease severity if applicable (e.g., stages of diabetic retinopathy).
4. Data Splitting: Divide the dataset into training, validation, and test sets (e.g., 70% for training, 15% for validation, 15% for testing).
5. Model Selection:
- Convolutional Neural Networks (CNNs)
- Transfer Learning (e.g., using pre-trained models like ResNet or Inception)
- Ensemble methods (e.g., Random Forests) for feature extraction and classification
6. Model Training: Train the selected model on the training dataset. Fine-tune hyperparameters, adjust learning rates, and implement data augmentation techniques to improve model performance.
7. Model Evaluation: Evaluate the trained model on the validation and test datasets using appropriate evaluation metrics such as accuracy, precision, recall, F1-score, and ROC AUC. Assess false positives and false negatives, as they have different clinical implications in blindness detection.
Client: Anaconda || Jupyter Notebook
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