Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Artificial intelligence (AI) has the potential to revolutionize disease diagnosis and management by performing classification difficult for human experts and by rapidly reviewing immense amounts of images. Despite its potential, clinical interpretability and feasible preparation of AI remains challenging. The traditional algorithmic approach to image analysis for classification previously relied on (1) handcrafted object segmentation, followed by (2) identification of each segmented object using statistical classifiers or shallow neural computational machine-learning classifiers designed specifically for each class of objects, and finally (3) classification of the image (Goldbaum et al., 1996xSee all ReferencesGoldbaum et al., 1996). Creating and refining multiple classifiers required many skilled people and much time and was computationally expensive (Chaudhuri et al., 1989xDetection of blood vessels in retinal images using two-dimensional matched filters. The development of convolutional neural network layers has allowed for significant gains in the ability to classify images and detect objects in a picture (Krizhevsky et al., 2017xImageNet classification with deep convolutional neural networks. These are multiple processing layers to which image analysis filters, or convolutions, are applied.