An imaging method is provided that includes: (i) providing a microscope having a lens and an autofocusing camera positioned adjacent to the microscope; (ii) positioning an illumination source adjacent to the microscope; (iii) moving a sample to a predefined offset position and illuminating the sample; (iv) acquiring an image of the illuminated sample via the autofocusing camera; and (v) utilizing a convolution neural network to identify an in-focus position of the sample. The convolution neural network may further include an input layer, output layer, and at least one hidden layer situated between the input and output layers. The hidden layer(s) may be selected from a group consisting of a convolution layer, pooling layer, normalization layer, fully connected layer, and a combination thereof. The convolution neural network may be trained to accurately define the weight to be applied to the layer(s). The illumination source may be a single-LED, dual-LED, LED array, Köhler illumination, and a combination thereof. The convolution neural network may advantageously predict the in-focus position of the acquired image without axial scanning and may be a multi-domain convolution neural network, which may receive input(s) selected from spatial features, Fourier transform of the acquired image, autocorrelation of the acquired image.