Enterprise AI Analysis
ResNet-EfficientNet powered framework for high-precision cough-based classification of infectious diseases
This report analyzes a deep learning framework designed for early and accurate detection of infectious diseases like COVID-19 through cough sound analysis. We detail its architecture, performance metrics, and implications for rapid, non-invasive screening.
Executive Impact: Pioneering Rapid Disease Detection
Leveraging cutting-edge deep learning models, this framework offers a transformative approach to infectious disease screening, providing high accuracy and efficiency for critical early intervention.
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Accuracy (ResNet-18)
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False Positive Rate (ResNet-18)
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Recall (ResNet-18)
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Cough Data Input
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Preprocessing
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Signal Augmentation
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Data Splitting
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Model Training (DL Models)
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Model Testing
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Interpretability & Analysis
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Classification Output
| Model | Accuracy (%) | Precision (%) | Recall (%) | F1_Score | MCC | False Positive Rate |
|---|---|---|---|---|---|---|
| 1D-CNN | 91.17 | 91.55 | 93.66 | 0.9259 | 0.8170 | 0.1240 |
| DS-CNN | 88.12 | 92.62 | 86.74 | 0.8958 | 0.7600 | 0.0992 |
| EfficientNet v2 | 95 | 92.45 | 98 | 0.9514 | 0.9026 | 0.08 |
| ResNet-18 | 98.5 | 98.99 | 98 | 0.9849 | 0.9699 | 0.01 |
ResNet-18 consistently outperforms other models across key metrics, demonstrating superior reliability and robustness for high-precision cough-based classification.
Rapid Screening for Infectious Diseases
Challenge: Traditional diagnostic tools for infectious diseases are often slow, costly, and inaccessible, especially in underdeveloped regions, hindering timely intervention and disease control.
Solution: The proposed deep learning framework leverages advanced models like ResNet-18 and EfficientNet v2 to provide an automated, high-precision method for classifying infectious diseases based on cough sounds.
Impact: This non-invasive, scalable solution enables rapid screening, achieving a 98.5% accuracy with ResNet-18 and a minimal false positive rate of 0.01. This facilitates early intervention, reduces transmission, and significantly improves patient care efficiency, even in resource-constrained environments.
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Implementation Timeline: From Concept to Clinical Impact
A typical phased approach to deploying AI-powered diagnostic solutions within an enterprise environment, tailored to ensure seamless integration and maximum benefit.
Phase 1: Data Preparation & Preprocessing (1-2 months)
Cleanse, normalize, and augment existing cough sound datasets. Establish data pipelines for continuous integration and ensure compliance with medical data standards.
Phase 2: Model Selection & Training (2-3 months)
Select and fine-tune deep learning models (ResNet-EfficientNet architectures) using augmented datasets. Conduct initial performance benchmarks and interpretability studies.
Phase 3: Integration & Validation (2-4 months)
Integrate the trained AI model into existing clinical systems or a new diagnostic platform. Perform rigorous validation with unseen real-world data and user acceptance testing.
Phase 4: Deployment & Monitoring (1-2 months)
Full-scale deployment of the AI diagnostic tool. Implement continuous monitoring for performance, accuracy, and system health. Provide ongoing support and updates.
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