Enhancing Radiology Diagnosis through Convolutional Neural Networks for Computer Vision in Healthcare

Abstract

Introduction: The transformative power of Convolutional Neural Networks (CNNs) in radiology diagnostics is examined in this study, with a focus on interpretability, effectiveness, and ethical issues.

Objectives: The primary objectives of this study include evaluating the performance of a CNN with an altered DenseNet architecture in terms of particularity, sensitivity, and accuracy. Comparative analyses are conducted to validate its superiority over conventional methods, emphasizing efficiency gains. The study also aims to address interpretability issues and explore the need for sophisticated methods and continuous model improvement.

Methods: The study employs an altered DenseNet architecture for the CNN and conducts comparative analyses to assess its performance in radiology diagnostics. The evaluation focuses on particularity, sensitivity, and accuracy, comparing the CNN with conventional methods..

Results. The CNN, with its altered DenseNet architecture, demonstrates admirable performance and a faster rate of convergence in radiology diagnostics, showcasing improvements in particularity, sensitivity, and accuracy compared to conventional methods. Comparative analyses validate the efficiency gains of the CNN. However, interpretability issues highlight the need for advanced methods and continuous model improvement.

Conclusions: In conclusion, the study emphasizes the transformative potential of CNNs in radiology diagnostics but recognizes the importance of addressing interpretability, integration, and ethical considerations. Future work should prioritize refining architectures, enhancing interpretability, and systematically addressing ethical implications for the responsible deployment of CNNs in radiology diagnostics. Collaborative efforts, continuous model improvement, and the development of extensive frameworks are essential for ensuring the ethical and effective use of CNNs in this critical medical field.