This research aims to improve the efficiency of skin lesion segment locations for the given input image of skin cancer using a combination of recently modified segmentation algorithms. Skin lesion segmentation is still a challenging task in medical image analysis because of the low contrast and high noise produced by dermoscopic imaging. Previous works extracted spatially-oriented information but failed in terms of training. They were based on convolutional neural networks (CNNs), which require extensive training time. Current results show 91% to 93% efficiency in segmentation, but the proposed segmentation capsule network (SegCaps) in this research has improved it up to 98% by adding four pre-processing sequential processes in combinations with SegCaps algorithms. The performance of the proposed SegCaps model was evaluated on two different datasets—ISBI 2017 and PH2 and implemented on the MatlabR2017b software. The chosen metrics were Jaccard co-efficient, dice similarity co-efficient, accuracy, sensitivity, and specificity for validation.