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Medical image segmentation aims to identify important or suspicious regions within medical images. However, many challenges are usually faced while developing networks for this type of analysis. First, preserving the original image resolution is of utmost importance for this task where identifying subtle features or abnormalities can significantly impact the accuracy of diagnosis. While introducing the dilated convolution improves the resolution of the convolutional neural network (CNN), it is not without shortcoming, i.e., the loss of local spatial resolution due to increased kernel sparsity in checkboard patterns. To address this shortcoming, we conceptualize a double-dilated convolution module for maintaining local spatial resolution while improving the receptive field size. Then, this approach is applied, as a proof-of-work, to tumor segmentation task in mammograms. In addition, our proposal also tackles the class …