ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence

<div>Qualitative results comparison on the Kvasir-SEG dataset. From the left: (1) image, (2) ground truth, (3) U-Net, (4) ResU-Net, (5) ResU-Net-mod, (6) ResU-Net++, and (7) U-Net (ours). From the experimental results, we can say that U-Net produces better segmentation masks than other competitors.</div>

Computational and Mathematical Methods in Medicine

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Figure 24

Figure 24: ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence 

Figure 24 | ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence 