A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computational Biology

Background:

• Infectious diseases pose a significant global health threat.
• Accurate and timely diagnosis is crucial for effective disease management and control.
• Artificial intelligence offers powerful tools for analyzing medical data and improving diagnostic capabilities.

Purpose of the Study:

• To detect and classify multiple infectious diseases using deep learning models.
• To evaluate the performance of various deep learning architectures on a diverse dataset of chest X-ray images.
• To identify the most effective deep learning model for infectious disease classification.

Main Methods:

• Utilized a dataset of 29,252 chest X-ray images covering COVID-19, tuberculosis, pneumonia, and other conditions.
• Employed deep learning models including EfficientNet, NASNetLarge, DenseNet169, ResNet152V2, and InceptionResNetV2.
• Applied image preprocessing techniques like augmentation and contrast enhancement, along with feature extraction methods.

Main Results:

• Exploratory data analysis was performed using RGB histograms to visualize pixel intensity and identify anomalies.
• The InceptionResNetV2 model demonstrated superior performance, achieving the highest accuracy of 88% during testing.
• The InceptionResNetV2 model also yielded the best loss value (0.399) and root mean square error (0.63).

Conclusions:

• Deep learning models show significant potential for the automated detection and classification of infectious diseases from medical images.
• The InceptionResNetV2 architecture is a highly effective model for this diagnostic task.
• AI-powered tools can enhance the speed and accuracy of diagnosing infectious diseases, aiding public health efforts.