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This paper describes a new Euclidean Similarity factor (ESF) based active contour model with deep learning for segmenting the tumor region into complete, core and enhanced tumor portions. Initially, the ESF considers the spatial distances and intensity differences of the region automatically to detect the tumor region. It preserves the image details but removes the noisy details. Then, the 3D Convolutional Neural Network (3D CNN) segments the tumor by automatically extracting spatiotemporal features. Finally, the extended shoelace method estimates the volume of the tumor accurately for [Formula: see text]-sided polygons. The simulation result achieves a high accuracy of 92% and Jaccard index of 0.912 and computes the tumor volume with effective performance than existing approaches. <\/jats:p>","DOI":"10.1142\/s1793962319500399","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T03:17:42Z","timestamp":1573528662000},"page":"1950039","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning network with Euclidean similarity factor for Brain MR Tumor segmentation and volume estimation"],"prefix":"10.1142","volume":"10","author":[{"given":"G.","family":"Anand Kumar","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. 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