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Inception-ResNet-V2

AS Anis Shazia
TX Tan Zi Xuan
JC Joon Huang Chuah
JU Juliana Usman
PQ Pengjiang Qian
KL Khin Wee Lai
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The basic building block of Inception-ResNet-V2 is called Residual Inception Block. A 1 × 1 convolution filter expansion layer is used after each block to scale up the filter bank dimensionality before the addition to match the depth of the input. This architecture uses batch normalization only on top of the traditional layers. Inception-ResNet-V2 is 164 layers deep and has an image input size of 299 × 299. The Residual Inception Block incorporates multiple-sized convolutional filters with residual connections. With the use of residual connections, this architecture prevents the problem of degradation due to deep networks and reduces the duration of training. Figure 4 explains our fine-tuned model of Inception-ResNet-V2 for COVID-19 and pneumonia classification.

Inception-ResNet-V2 architecture designed for binary classification

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