Title : Enhanced detection and prediction of viral infectious diseases using extended convolutional neural networks: Addressing challenges in early diagnosis and pathogen variability
Abstract:
Infectious diseases continue to pose a major global health threat, made worse by the continuous emergence of new pathogens, especially viruses, and the increasing spread of antibiotic-resistant strains. Viral infections, in particular, are a serious concern due to their ability to spread rapidly and mutate, often resulting in significant public health crises, as demonstrated by the COVID-19 pandemic. Detecting viral infections promptly and accurately is essential to controlling outbreaks and minimizing their impact. However, traditional diagnostic methods are often slow, less accurate, and struggle to adapt to new or evolving pathogens. These limitations have fueled the growing interest in artificial intelligence (AI), particularly deep learning techniques like Convolutional Neural Networks (CNNs), which offer promising solutions to these challenges. This presentation introduces a robust approach for detecting and predicting viral infectious diseases using extended Convolutional Neural Networks (CNNs). The study tackles key issues such as early diagnosis, pathogen variability, and the need for models that can generalize effectively across diverse datasets. Traditional diagnostic techniques often fall short due to the variation in symptoms among viral diseases, delayed immune responses, and the difficulty in distinguishing between similar pathogens. While laboratory methods like polymerase chain reaction (PCR) and antigen testing provide reliable results, they are time-consuming and depend on significant infrastructure. Additionally, the constant evolution of viral strains and the emergence of new pathogens complicate both diagnosis and prediction, leading to delayed treatment and faster disease spread. Extended CNN architectures provide a cutting-edge solution by automating feature extraction from complex data, identifying hierarchical patterns, and processing large-scale medical images, patient records, and diagnostic information. These enhanced CNNs go beyond standard models by incorporating extra layers and components that improve their ability to extract features from diverse inputs, such as genomic sequences, clinical images, and epidemiological data. The proposed extended CNN model aims to address issues like data imbalance, overfitting, and the demand for real-time prediction in clinical settings.
The research focuses on how extended CNN models can boost early diagnosis accuracy by detecting subtle patterns in patient data, enabling the detection of infections even at early stages. It also explores how these models can be optimized to accommodate the variability in viral pathogens, including their mutations and the rise of antibiotic-resistant strains. Advanced techniques like data augmentation, transfer learning, and domain adaptation further strengthen the model's ability to handle a wide range of viral datasets. Expected outcomes from this research include significant advancements in the detection and prediction of viral infectious diseases, particularly through reductions in false positives and false negatives, improved model interpretability, and faster diagnostics. Additionally, the presentation will discuss the potential integration of extended CNNs into existing healthcare systems and their role in enhancing public health efforts such as outbreak management and disease surveillance. By overcoming the current challenges in viral diagnosis and pathogen variability, this research seeks to develop more efficient, scalable, and flexible healthcare solutions that can better address future infectious disease outbreaks.
