  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. With an altered DenseNet architecture, the
CNN performs admirably in terms of particularity, sensitivity, as well as
accuracy. Its superiority over conventional methods is validated by comparative
analyses, which highlight efficiency gains. Nonetheless, interpretability
issues highlight the necessity of sophisticated methods in addition to
continuous model improvement. Integration issues like interoperability and
radiologists' training lead to suggestions for teamwork. Systematic
consideration of the ethical implications is carried out, necessitating
extensive frameworks. Refinement of architectures, interpretability, alongside
ethical considerations need to be prioritized in future work for responsible
CNN deployment in radiology diagnostics.
