{"id":"https://openalex.org/W4388937278","doi":"https://doi.org/10.1109/icccnt56998.2023.10308004","title":"Inception Inspired U-Net for Effective Segmentation of Microscopy Images","display_name":"Inception Inspired U-Net for Effective Segmentation of Microscopy Images","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388937278","doi":"https://doi.org/10.1109/icccnt56998.2023.10308004"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10308004","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10308004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064788147","display_name":"Aruna Kumari Kakumani","orcid":"https://orcid.org/0000-0002-1040-1663"},"institutions":[{"id":"https://openalex.org/I2801452159","display_name":"Vignana Jyothi Institute of Management","ror":"https://ror.org/02ken4f88","country_code":"IN","type":"education","lineage":["https://openalex.org/I2801452159"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aruna Kumari Kakumani","raw_affiliation_strings":["VNR Vignana Jyothi Institute of Engineering and Technology,Dept. of ECE,Hyderabad,India","Dept. of ECE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VNR Vignana Jyothi Institute of Engineering and Technology,Dept. of ECE,Hyderabad,India","institution_ids":["https://openalex.org/I2801452159"]},{"raw_affiliation_string":"Dept. of ECE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India","institution_ids":["https://openalex.org/I2801452159"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045089285","display_name":"L. Padma Sree","orcid":null},"institutions":[{"id":"https://openalex.org/I2801452159","display_name":"Vignana Jyothi Institute of Management","ror":"https://ror.org/02ken4f88","country_code":"IN","type":"education","lineage":["https://openalex.org/I2801452159"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"L. Padma Sree","raw_affiliation_strings":["VNR Vignana Jyothi Institute of Engineering and Technology,Dept. of ECE,Hyderabad,India","Dept. of ECE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VNR Vignana Jyothi Institute of Engineering and Technology,Dept. of ECE,Hyderabad,India","institution_ids":["https://openalex.org/I2801452159"]},{"raw_affiliation_string":"Dept. of ECE, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India","institution_ids":["https://openalex.org/I2801452159"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2801452159"],"apc_list":null,"apc_paid":null,"fwci":0.1613,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57697962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"9351","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7460533380508423},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7210453152656555},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6762512922286987},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6188060641288757},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5762187838554382},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5224175453186035},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.472280353307724},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46524250507354736},{"id":"https://openalex.org/keywords/microscopy","display_name":"Microscopy","score":0.45701295137405396},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.44910168647766113},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.42443227767944336},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.28151828050613403},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.128480464220047},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11297538876533508},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.080118328332901},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.07507067918777466},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.07400235533714294}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7460533380508423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7210453152656555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6762512922286987},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6188060641288757},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5762187838554382},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5224175453186035},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.472280353307724},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46524250507354736},{"id":"https://openalex.org/C147080431","wikidata":"https://www.wikidata.org/wiki/Q1074953","display_name":"Microscopy","level":2,"score":0.45701295137405396},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.44910168647766113},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.42443227767944336},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28151828050613403},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.128480464220047},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11297538876533508},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.080118328332901},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.07507067918777466},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.07400235533714294},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10308004","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10308004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2017951283","https://openalex.org/W2097117768","https://openalex.org/W2168182874","https://openalex.org/W2183341477","https://openalex.org/W2618530766","https://openalex.org/W2758694956","https://openalex.org/W2903554604","https://openalex.org/W2961441251","https://openalex.org/W2963180316","https://openalex.org/W2964350391","https://openalex.org/W3006169930","https://openalex.org/W3010841569","https://openalex.org/W3014304846","https://openalex.org/W3045567051","https://openalex.org/W3110613120","https://openalex.org/W3149937760","https://openalex.org/W3153388040","https://openalex.org/W3159895826","https://openalex.org/W4220790466","https://openalex.org/W4304092634","https://openalex.org/W4312831651","https://openalex.org/W4361733287"],"related_works":["https://openalex.org/W3104750253","https://openalex.org/W3021239166","https://openalex.org/W2586273397","https://openalex.org/W4366341510","https://openalex.org/W2390936256","https://openalex.org/W2483429559","https://openalex.org/W2016385589","https://openalex.org/W2009559548","https://openalex.org/W2906397153","https://openalex.org/W2385445039"],"abstract_inverted_index":{"Biomedical":[0],"image":[1],"analysis":[2,13],"has":[3],"vital":[4],"role":[5],"in":[6,46],"medical":[7],"diagnosis.":[8],"Computerized":[9],"tools":[10],"for":[11,20,33,50,75,90],"automatic":[12,34],"of":[14,36,62],"biomedical":[15],"images":[16],"helps":[17],"the":[18,21,76,91],"radiologist":[19],"disease":[22],"identification.":[23],"This":[24,53],"work":[25],"explores":[26],"a":[27],"custom":[28],"designed":[29,55],"deep":[30,40],"learning":[31,41],"architecture":[32,42],"segmentation":[35,67],"microscopy":[37,63],"images.":[38],"The":[39,69],"uses":[43],"Inception":[44],"modules":[45],"U-Net":[47],"type":[48],"structure":[49],"effective":[51],"segmentation.":[52],"newly":[54],"framework":[56],"is":[57],"tested":[58],"on":[59],"different":[60],"types":[61],"data":[64],"to":[65],"assess":[66],"performance.":[68],"proposed":[70],"model\u2019s":[71],"dice":[72],"similarity":[73],"index":[74],"DIC-C2DH-HELA":[77,92],"and":[78,83,93,98],"Fluo-C2DL-MSC":[79,94],"datasets":[80,95],"are":[81,96],"0.9559":[82],"0.9167":[84],"respectively.":[85,100],"Further":[86],"intersection":[87],"over":[88],"union":[89],"0.8783":[97],"0.74779":[99]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
