AN IMPROVED BUTTERFLY-NET BASED DETECTION OF STONES IN KIDNEY IMAGES

Kidney stone disease is one of the major life threatening problem existing worldwide. It is important to recognize the true and exact location of kidney stone. The ultrasound images of kidney comprise speckle noise and are of low contrast which makes the identification of kidney abnormalities a challenging task. Thus, the doctors may find recognition of small stones difficult. Butterfly-net model is a low-complexity convolutional neural network with sparse cross channel connections and with pre-processing techniques for kidney stone detection. IntegratingSerious problems are well approximated with network complexity based on frequency bandwidth rather than input dimension using a butterfly-net with a fully connected neural network. The proposed Butterfly-net obviously embraces the arranged construction in the Butterfly plot as the stacked convolutional layers. In the event that the boundaries are hard-coded as that in the Butterfly plot, Butterfly-net on the whole registers the Fourier coefficients of the info signal with ensured mathematical exactness.