Deep learning algorithms already support medical imaging in many areas today and include all methods, such as X-ray, ultrasound, CT and MRI examinations. The increasing automation of diagnostic procedures offers advantages for patients and physicians alike: they increase the accuracy and reliability of examination results, facilitate diagnostic statements and enable improved, individually tailored therapeutic measures.
The basic prerequisite for the use of artificial neural networks is in many cases machine vision. In our White Paper, Basler Product Manager Peter Behringer explains the four steps of a typical image processing process and compares the three types of vision systems for deep learning: embedded systems, PC-based systems, and FPGA frame grabber-based systems.
Would you like to learn more about deep learning in the context of machine vision? Our White Paper “Artificial Intelligence in Image Processing ” compares Deep Learning-based functions with conventional methods of image analysis.
