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Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
The study proposes a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images.
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy
Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients.
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence.
Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network
The scheme first transforms the noisy Raman spectrum to a two-dimensional scale map using CWT.
Method for classifying a noisy Raman spectrum based on a wavelet transform and a deep neural network
The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectrums (10-30 dB noise power).
