An Interpretable and Accurate Deep-Learning
ABSTARCT :
The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability.
In general, accurate deep-learning classifiers provide little model interpretability, while interpretable models do not have competitive classification accuracy.
we introduce a new deep-learning diagnosis framework, called InterNRL, that is designed to be highly accurate and interpretable.
InterNRL consists of a student-teacher framework, where the student model is an interpretable prototype-based classifier (ProtoPNet) and the teacher is an accurate global image classifier (GlobalNet).
EXISTING SYSTEM :
DEEP learning has recently shown tremendous success in many automated image analysis tasks.
A typical representative of deep learning models is the deep neural network (DNN) which can automatically learn hierarchical features from image input.
DNNs are commonly composed of many interconnected layers with a massive number of learnable parameters, making it hard to interpret their predictions.
DISADVANTAGE :
This suggests that the framework is designed in a way that its decisions can be understood by humans, which is important for trust and validation.
This indicates that the framework is expected to produce correct results with high reliability.
This implies that the framework uses deep learning techniques to diagnose a particular problem or condition. Deep learning involves neural networks with many layers that can model complex patterns in data.
This is a type of machine learning where the model is trained on a labeled dataset, meaning each training example is paired with an output label.
PROPOSED SYSTEM :
High accuracy on training data does not always translate to good performance on unseen data. Models need to be carefully regularized to avoid overfitting.
Accuracy heavily depends on the quality and quantity of data. Poor or insufficient data can lead to inaccurate predictions.
Deep learning models require significant computational power and resources for training, which can be expensive and time-consuming.
These models typically require large amounts of data to perform well, which may not be available in all domains.
ADVANTAGE :
Collect data from various sources such as medical records, sensor data, images, etc.
Remove noise and handle missing values to ensure data quality.
Normalize or standardize data to ensure consistency across different scales.
Acquire a dataset with fully labeled examples for initial model training.
Collect additional data where only some examples are labeled to leverage semi-supervised learning.
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