{'fraction_non_alphanumeric': 0.027745664739884393, 'fraction_numerical': 0.003468208092485549, 'mean_word_length': 5.185714285714286, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A W-Net Based Architecture with Residual Block for Liver Segmentation

One of the traditionally used methods for segmenting images is by using a convolutional neural network (CNN). CNN is helpful in various applications like object detection, image recognition, optical character recognition (OCR), and image segmentation. Consequently, this research work has designed a new model by utilizing similar CNN-based architecture as a modification of W-Net. Specifically, the proposed approach adds the residual block in the existing W-Net architecture to achieve a higher dice score. This approach employs an efficient image segmentation technique for detecting tumor cells in the liver. The result shows that the proposed technique not only detects a tumor in different datasets, such as LiTS17 & 3DIRCADb, but also performs better when compared to state-of-art works.