Model Purpose: Classification of cataract disease using convolutional neural networks (CNNs).
Data Source: Cataract Eye Dataset providing direct eye images for image classification.
Model Architecture: DenseNet121 CNN architecture emerged as the forefront model for cataract disease detection.
Training Dataset Size: The model is trained using 443 images.
The model utilizes DenseNet121 architecture implemented using the TensorFlow object detection framework for cataract disease detection.
The process involves training and validation stages, adjusting hyperparameters, and optimizing model learning.
The dense block with 512 hidden layers and the "relu" activation function is crucial for model performance.
The final layer incorporates a sigmoid activation function for output between 0 and 1.