Improving Medical Imaging Diagnostics with Deep Convolutional Networks for Early Detection and Treatment

Diagnostic imaging is an important technique which helps in diagnosing several diseases at the initial stage in order to take necessary treatments. Nevertheless, conventional techniques tend to have issues with regard to precision, speed, and quantitatively assessing multidimensional medical
information. In this paper, we reveal the potential of deep
convolutional networks (DCNs) in the field of medical image
diagnostic. It means that, with the help of deep learning
algorithms DCNs can learn meaningful features of medical
images and improve diagnostic accuracy and speed. The near
proposed technique involves the use of deep convolutional
network model to analyse medical images for diagnosis of
diseases such as cancer, heart diseases and neurological
disorders. The obtained experimental results demonstrate that the presented model has the highest accuracy of 96.5% with very high sensitivity and specificity coefficients compared to other methods. Further, the interpretability of the proposed DCN model and different aspects of it are discussed to support clinical validation. Through this research, the effectiveness of deep learning in revolutionizing the diagnosis for medical imaging diagnostics while early disease detection and
treatment is established.