An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal 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 :
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.
DISADVANTAGE :
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.
PROPOSED SYSTEM :
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.
ADVANTAGE :
Interpretable models allow users and stakeholders to understand how decisions are made, which builds trust and facilitates validation of the model’s results.
In fields like healthcare and finance, interpretable models help meet regulatory requirements that demand explainability in automated decision-making processes.
High accuracy ensures that the framework provides dependable and precise diagnoses, which is critical in high-stakes applications such as medical diagnosis.
Accurate diagnoses can lead to better decision-making and improved outcomes, whether in healthcare, predictive maintenance, or other applications.
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