  Medical image segmentation is vital to the area of medical imaging because it
enables professionals to more accurately examine and understand the information
offered by different imaging modalities. The technique of splitting a medical
image into various segments or regions of interest is known as medical image
segmentation. The segmented images that are produced can be used for many
different things, including diagnosis, surgery planning, and therapy
evaluation.
  In initial phase of research, major focus has been given to review existing
deep-learning approaches, including researches like MultiResUNet, Attention
U-Net, classical U-Net, and other variants. The attention feature vectors or
maps dynamically add important weights to critical information, and most of
these variants use these to increase accuracy, but the network parameter
requirements are somewhat more stringent. They face certain problems such as
overfitting, as their number of trainable parameters is very high, and so is
their inference time.
  Therefore, the aim of this research is to reduce the network parameter
requirements using depthwise separable convolutions, while maintaining
performance over some medical image segmentation tasks such as skin lesion
segmentation using attention system and residual connections.
