  Accurate identification and precise delineation of regions of significance,
such as tumors or lesions, is a pivotal goal in medical imaging analysis. This
paper proposes SPEEDNet, a novel architecture for precisely segmenting lesions
within colonoscopy images. SPEEDNet uses a novel block named
Dilated-Involutional Pyramidal Convolution Fusion (DIPC). A DIPC block combines
the dilated involution layers pairwise into a pyramidal structure to convert
the feature maps into a compact space. This lowers the total number of
parameters while improving the learning of representations across an optimal
receptive field, thereby reducing the blurring effect. On the EBHISeg dataset,
SPEEDNet outperforms three previous networks: UNet, FeedNet, and AttesResDUNet.
Specifically, SPEEDNet attains an average dice score of 0.952 and a recall of
0.971. Qualitative results and ablation studies provide additional insights
into the effectiveness of SPEEDNet. The model size of SPEEDNet is 9.81 MB,
significantly smaller than that of UNet (22.84 MB), FeedNet(185.58 MB), and
AttesResDUNet (140.09 MB).
