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Chapter 1 Congruence of deep learning in biomedical engineering 7
Figure 1.6 SqueezeNet architecture [37,38]. From Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han,
William J. Dally and Kurt Keutzer, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,
conference paper, ICLR 2017, arXiv:1602.07360 [cs.CV].
cosine transform (DCT), to implant the data in the pictures.
Another is the utilization of wavelet changes, for example, discrete
wavelet transform (DWT) or integer wavelet transform (IWT). We
have utilized IWT in our proposed strategy. In this proposal, at
first a number of steganography strategies are dissected.
