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. Author manuscript; available in PMC: 2023 Dec 1.
Published in final edited form as: J Signal Process Syst. 2022 Apr 12;94(12):1515–1529. doi: 10.1007/s11265-022-01758-3

Fig. 1.

Fig. 1.

(a) Schematic of GoogleNet architecture, (b) detail of an inception (layer IN3a) block, (c) modification of last three layers of GoogleNet architecture to adopt the number of target classes for AAV classification problem. Here, CNV: convolution layer, MP: max pooling layer, IN: inception block, AP: average pooling layer, DO: dropout layer, FC: fully connected layer, SM: softmax layer, DC: depth concatenation layer. The red dot (∙) represents a ReLU operation and the red-cross (×) indicates a cross channel normalization operation.