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Many gastrointestinal tumor diagnosis methods were developed, but all of these methods used manual contour rather than automatic segmentation. The research adopts enhanced automatic segmentation to improve the diagnosis of the gastrointestinal stromal tumor with deep convolutional neural networks. This solution\u2019s proposed system is an enhanced automated segmentation methodology using multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution, which segments the ultrasound image automatically into the region of interest (the infected area). Convolutional Neural Network with Class Activation Mapping is done to diagnose an image with the tumor for Four datasets, namely (USS1, SH Hospital, SNUH, BUSI). This proposed system helps to get a clearer tumor image, and the accuracy has increased from 84.275% to 88.4%, and the processing time has reduced from 28.525% to 24.575%. The proposed solution enhanced Automatic Segmentation helped to get clearer tumor image which resulted in increased accuracy and decreased performance time compared to the state-of-the-art. Automatic segmentation overcomes the dependency on the expert for drawing the Region of Interest (ROI).<\/jats:p>","DOI":"10.1007\/s11042-022-11936-x","type":"journal-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T12:04:53Z","timestamp":1647605093000},"page":"23845-23865","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor"],"prefix":"10.1007","volume":"81","author":[{"given":"Sanira","family":"Tuladhar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-3540","authenticated-orcid":false,"given":"Abeer","family":"Alsadoon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. W. 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