Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network
A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia u...
Gespeichert in:
| Veröffentlicht in: | Electronics (Basel) 2023-03, Vol.12 (5), p.1116 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 5 |
| container_start_page | 1116 |
| container_title | Electronics (Basel) |
| container_volume | 12 |
| creator | Ansari, Sanam Navin, Ahmad Habibizad Babazadeh Sangar, Amin Vaez Gharamaleki, Jalil Danishvar, Sebelan |
| description | A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods. |
| doi_str_mv | 10.3390/electronics12051116 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2785187415</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A741740098</galeid><sourcerecordid>A741740098</sourcerecordid><originalsourceid>FETCH-LOGICAL-c389t-e8cb2f701c9145798668227982b924d59397e70fa0d6b9a2527e3cda4f9bb30c3</originalsourceid><addsrcrecordid>eNptUU1LxDAQLaKg6P4CLwHPXfPRbprj-rG6UPXinkuaTmp0m9SkRbq_3ogLKjhzmHnDezPwJknOCZ4zJvAlbEEN3lmjAqE4J4QsDpITirlIBRX08Fd_nMxCeMUxBGEFwyeJXqpxAFTC-AadkejGyNa6YAK6kgEa5Cxad7KFgJxG5dT1L05NQ4TSNujB2T3aBGNb9Dz1kK7XaDXudhO6AejRIwwfzr-dJUdabgPM9vU02axun6_v0_Lpbn29LFPFCjGkUKiaao6JEiTLuSgWi4LSWGktaNbkggkOHGuJm0UtJM0pB6YamWlR1wwrdppcfO_tvXsfIQzVqxu9jScryoucFDwj-Q-rlVuojNVu8FJ1JqhqGQk8i_4UkTX_hxWziU4pZ0GbOP8jYN8C5V0IHnTVe9NJP1UEV1-vqv55FfsE3AqH1Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2785187415</pqid></control><display><type>article</type><title>Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Ansari, Sanam ; Navin, Ahmad Habibizad ; Babazadeh Sangar, Amin ; Vaez Gharamaleki, Jalil ; Danishvar, Sebelan</creator><creatorcontrib>Ansari, Sanam ; Navin, Ahmad Habibizad ; Babazadeh Sangar, Amin ; Vaez Gharamaleki, Jalil ; Danishvar, Sebelan</creatorcontrib><description>A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12051116</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Acute leukemia ; Algorithms ; Blood ; Bone marrow ; Cancer ; Cancer therapies ; Classification ; Computer-aided medical diagnosis ; Diagnosis ; Disease ; Epidemiology ; Feature extraction ; Health aspects ; Hydrocarbons ; Image processing ; Leukemia ; Lymphocytes ; Machine learning ; Medical imaging ; Methods ; Monocytes ; Morphology ; Neural networks ; Radiation ; Stem cells ; Tumors</subject><ispartof>Electronics (Basel), 2023-03, Vol.12 (5), p.1116</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-e8cb2f701c9145798668227982b924d59397e70fa0d6b9a2527e3cda4f9bb30c3</citedby><cites>FETCH-LOGICAL-c389t-e8cb2f701c9145798668227982b924d59397e70fa0d6b9a2527e3cda4f9bb30c3</cites><orcidid>0000-0002-5190-8460 ; 0000-0002-8258-0437</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Ansari, Sanam</creatorcontrib><creatorcontrib>Navin, Ahmad Habibizad</creatorcontrib><creatorcontrib>Babazadeh Sangar, Amin</creatorcontrib><creatorcontrib>Vaez Gharamaleki, Jalil</creatorcontrib><creatorcontrib>Danishvar, Sebelan</creatorcontrib><title>Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network</title><title>Electronics (Basel)</title><description>A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods.</description><subject>Accuracy</subject><subject>Acute leukemia</subject><subject>Algorithms</subject><subject>Blood</subject><subject>Bone marrow</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Classification</subject><subject>Computer-aided medical diagnosis</subject><subject>Diagnosis</subject><subject>Disease</subject><subject>Epidemiology</subject><subject>Feature extraction</subject><subject>Health aspects</subject><subject>Hydrocarbons</subject><subject>Image processing</subject><subject>Leukemia</subject><subject>Lymphocytes</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Methods</subject><subject>Monocytes</subject><subject>Morphology</subject><subject>Neural networks</subject><subject>Radiation</subject><subject>Stem cells</subject><subject>Tumors</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUU1LxDAQLaKg6P4CLwHPXfPRbprj-rG6UPXinkuaTmp0m9SkRbq_3ogLKjhzmHnDezPwJknOCZ4zJvAlbEEN3lmjAqE4J4QsDpITirlIBRX08Fd_nMxCeMUxBGEFwyeJXqpxAFTC-AadkejGyNa6YAK6kgEa5Cxad7KFgJxG5dT1L05NQ4TSNujB2T3aBGNb9Dz1kK7XaDXudhO6AejRIwwfzr-dJUdabgPM9vU02axun6_v0_Lpbn29LFPFCjGkUKiaao6JEiTLuSgWi4LSWGktaNbkggkOHGuJm0UtJM0pB6YamWlR1wwrdppcfO_tvXsfIQzVqxu9jScryoucFDwj-Q-rlVuojNVu8FJ1JqhqGQk8i_4UkTX_hxWziU4pZ0GbOP8jYN8C5V0IHnTVe9NJP1UEV1-vqv55FfsE3AqH1Q</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Ansari, Sanam</creator><creator>Navin, Ahmad Habibizad</creator><creator>Babazadeh Sangar, Amin</creator><creator>Vaez Gharamaleki, Jalil</creator><creator>Danishvar, Sebelan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-5190-8460</orcidid><orcidid>https://orcid.org/0000-0002-8258-0437</orcidid></search><sort><creationdate>20230301</creationdate><title>Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network</title><author>Ansari, Sanam ; Navin, Ahmad Habibizad ; Babazadeh Sangar, Amin ; Vaez Gharamaleki, Jalil ; Danishvar, Sebelan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-e8cb2f701c9145798668227982b924d59397e70fa0d6b9a2527e3cda4f9bb30c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Acute leukemia</topic><topic>Algorithms</topic><topic>Blood</topic><topic>Bone marrow</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Classification</topic><topic>Computer-aided medical diagnosis</topic><topic>Diagnosis</topic><topic>Disease</topic><topic>Epidemiology</topic><topic>Feature extraction</topic><topic>Health aspects</topic><topic>Hydrocarbons</topic><topic>Image processing</topic><topic>Leukemia</topic><topic>Lymphocytes</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Methods</topic><topic>Monocytes</topic><topic>Morphology</topic><topic>Neural networks</topic><topic>Radiation</topic><topic>Stem cells</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ansari, Sanam</creatorcontrib><creatorcontrib>Navin, Ahmad Habibizad</creatorcontrib><creatorcontrib>Babazadeh Sangar, Amin</creatorcontrib><creatorcontrib>Vaez Gharamaleki, Jalil</creatorcontrib><creatorcontrib>Danishvar, Sebelan</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ansari, Sanam</au><au>Navin, Ahmad Habibizad</au><au>Babazadeh Sangar, Amin</au><au>Vaez Gharamaleki, Jalil</au><au>Danishvar, Sebelan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network</atitle><jtitle>Electronics (Basel)</jtitle><date>2023-03-01</date><risdate>2023</risdate><volume>12</volume><issue>5</issue><spage>1116</spage><pages>1116-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics12051116</doi><orcidid>https://orcid.org/0000-0002-5190-8460</orcidid><orcidid>https://orcid.org/0000-0002-8258-0437</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2079-9292 |
| ispartof | Electronics (Basel), 2023-03, Vol.12 (5), p.1116 |
| issn | 2079-9292 2079-9292 |
| language | eng |
| recordid | cdi_proquest_journals_2785187415 |
| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Accuracy Acute leukemia Algorithms Blood Bone marrow Cancer Cancer therapies Classification Computer-aided medical diagnosis Diagnosis Disease Epidemiology Feature extraction Health aspects Hydrocarbons Image processing Leukemia Lymphocytes Machine learning Medical imaging Methods Monocytes Morphology Neural networks Radiation Stem cells Tumors |
| title | Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T05%3A39%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Acute%20Leukemia%20Diagnosis%20Based%20on%20Images%20of%20Lymphocytes%20and%20Monocytes%20Using%20Type-II%20Fuzzy%20Deep%20Network&rft.jtitle=Electronics%20(Basel)&rft.au=Ansari,%20Sanam&rft.date=2023-03-01&rft.volume=12&rft.issue=5&rft.spage=1116&rft.pages=1116-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics12051116&rft_dat=%3Cgale_proqu%3EA741740098%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2785187415&rft_id=info:pmid/&rft_galeid=A741740098&rfr_iscdi=true |