Deep Learning Techniques for Enhanced Diagnostic Precision in Radiology

Abstract

Radiology is an important part of modern diagnosis, but it's still hard to accurately read medical images because there's so much data and it's so complicated. This study presents a framework for deep learning that integrates a bespoke convolutional neural network, also called a CNN, in ResNet50based transfer learning in order to improve diagnostic accuracy in radiology. The custom CNN finds out hierarchical spatial characteristics from radiological images. ResNet50, on the other hand, uses weights that were previously trained to make the model converge, generalize, and become more accurate overall. Tests on several radiology datasets suggest the proposed method works better, with an accuracy rate of over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 1 \%}$</tex>. Grad-CAM visualizations show that the models emphasize clinically important areas, which makes them easier to understand and more likely to be used in clinical practice. These results indicate that the combination of transfer learning with meticulously crafted CNN architectures may provide a dependable, efficient, and clinically relevant instrument for autonomous radiological diagnosis.

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