![back to the future 3 train model back to the future 3 train model](https://i0.wp.com/thelegalgeeks.com/wp-content/uploads/2013/09/Train_2405_Final.jpg)
Familiarity with this state-of-the-art methodology would help not only researchers who apply CNN to their tasks in radiology and medical imaging, but also clinical radiologists, as deep learning may influence their practice in the near future.
![back to the future 3 train model back to the future 3 train model](https://wallpapers.gg/wp-content/uploads/2017/09/Back-to-the-Future-DeLorean-At-Night-1920x1080.jpg)
Needless to say, there has been a surge of interest in the potential of CNN among radiology researchers, and several studies have already been published in areas such as lesion detection, classification, segmentation, image reconstruction, and natural language processing.
#Back to the future 3 train model skin#
demonstrated the potential of deep learning for diabetic retinopathy screening, skin lesion classification, and lymph node metastasis detection, respectively. Medical research is no exception, as CNN has achieved expert-level performances in various fields. The most established algorithm among various deep learning models is convolutional neural network (CNN), a class of artificial neural networks that has been a dominant method in computer vision tasks since the astonishing results were shared on the object recognition competition known as the ImageNet Large Scale Visual Recognition Competition (ILSVRC) in 2012.