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      Hyperspectral images classification for white blood cells with attention-aided convolutional neural networks and fusion network

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Deep Residual Learning for Image Recognition

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              Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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                Author and article information

                Journal
                Journal of Modern Optics
                Journal of Modern Optics
                Informa UK Limited
                0950-0340
                1362-3044
                March 30 2023
                August 30 2023
                March 30 2023
                : 70
                : 6
                : 364-376
                Affiliations
                [1 ]School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, People’s Republic of China
                [2 ]Clinical Laboratory, The Second Affiliated Hospital of Dalian Medical University, Dalian Medical University, Dalian, People’s Republic of China
                Article
                10.1080/09500340.2023.2248634
                ad28a040-29dc-4bf6-a455-50804e2bc9d5
                © 2023
                History

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