Advancing Crayfish Disease Detection: A Comparative Study of Deep Learning and Canonical Machine Learning Techniques

Keyphrases

• Machine Learning Techniques 100%
• Deep Learning 100%
• Disease Diagnosis 100%
• Crayfish Plague 100%
• Crayfish 75%
• Detection Accuracy 50%
• Hybrid Model 50%
• Deep Learning Model 50%
• ConvNeXt 50%
• ResNet50 Model 50%
• RF Algorithm 50%
• AdamW 50%
• T-S Model 50%
• Hybrid Method 25%
• High Performance 25%
• Statistical Significance 25%
• Machine Learning Algorithms 25%
• Machine Learning Models 25%
• Performance Difference 25%
• Model Performance 25%
• Early Detection 25%
• Detection Sensitivity 25%
• Population Decline 25%
• Imbalanced Data 25%
• ResNet50 25%
• Automatic Feature Extraction 25%
• Canonical Model 25%
• Sample Detection 25%
• Ecosystem Balance 25%

INIS

• diseases 100%
• learning 100%
• machine learning 100%
• detection 100%
• hybrids 80%
• performance 60%
• algorithms 60%
• populations 40%
• values 20%
• ecosystems 20%
• balances 20%
• size 20%
• sensitivity 20%
• weight 20%
• accuracy 20%
• datasets 20%
• extraction 20%
• photographs 20%

Chemical Engineering

• Learning System 100%
• Deep Learning Method 100%