Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework

Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. The conventional diagnostic tools, based on the morphological and cytochemical investigation of b...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Iraqi journal for computers and informatics (Online) Jg. 50; H. 2; S. 186 - 206
1. Verfasser: Mansoor Alhammadi, Mohammed
Format: Journal Article
Sprache:Arabisch
Englisch
Veröffentlicht: University of Information Technology and Communications 30.12.2024
Schlagworte:
acute lymphoblastic leukemia, convolutional autoencoder, convolutional neural network, feature extraction, computer-aided diagnosis
ISSN:2313-190X, 2520-4912
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. The conventional diagnostic tools, based on the morphological and cytochemical investigation of blood and bone smears, are usually not specific and laborious. Thus, they often result in diagnostic errors and delay in treatment initiation. In this paper, ALL-diagnosing methods based on the convolutional autoencoder (CAE) was proposed to reduce the amount of data, and then convolutional neural network (CNN) was applied to identify ALL. The design method employed deep neural networks to recognize the features of the cells in question and then distinguish them as either leukemic or healthy cell types. The proposed laboratory method, with the use of the curated datasets of annotated pathological images of normal lymphoid progenitor cells, aimed to tackle the challenges related to the lack of curated datasets with annotated images of these cells. These challenges are believed to be linked to imprecise and time-consuming leukemia diagnosis and cure process. The simulated results confirmed the efficiency of the suggested technique, where CAE showed a correlation coefficient of 0.987 for lymphoblastic cells and CNN had an accuracy rate of 99.92% in ALL diagnosis. Such data demonstrated the capability of deep-based methodologies to fight leukemia.
AbstractList Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. The conventional diagnostic tools, based on the morphological and cytochemical investigation of blood and bone smears, are usually not specific and laborious. Thus, they often result in diagnostic errors and delay in treatment initiation. In this paper, ALL-diagnosing methods based on the convolutional autoencoder (CAE) was proposed to reduce the amount of data, and then convolutional neural network (CNN) was applied to identify ALL. The design method employed deep neural networks to recognize the features of the cells in question and then distinguish them as either leukemic or healthy cell types. The proposed laboratory method, with the use of the curated datasets of annotated pathological images of normal lymphoid progenitor cells, aimed to tackle the challenges related to the lack of curated datasets with annotated images of these cells. These challenges are believed to be linked to imprecise and time-consuming leukemia diagnosis and cure process. The simulated results confirmed the efficiency of the suggested technique, where CAE showed a correlation coefficient of 0.987 for lymphoblastic cells and CNN had an accuracy rate of 99.92% in ALL diagnosis. Such data demonstrated the capability of deep-based methodologies to fight leukemia.
Author Mansoor Alhammadi, Mohammed
Author_xml – sequence: 1
  givenname: Mohammed
  surname: Mansoor Alhammadi
  fullname: Mansoor Alhammadi, Mohammed
BookMark eNo9kEtPAjEUhRujiYis3fYPDPQxnceSjPhICG4kMW6a284tFhhKWsTw7x3RuDo35yZfTr4bcrkLOyTkjrOxULxWE7-2fnxUzIuxYuKCDIQSLMtrLi77W3KZ8Zq9XZNRSt4wKStVMFkPyHsTuv3nAWM29S229N7DaheSTzQ4OrX9h85P3f4jmC2kg7d0jp8b7DyYE10mv1tRoItwxC1tprOsWSzoQ4QOv0Lc3JIrB9uEo78ckuXD7LV5yuYvj8_NdJ5Znpciq1VpRI7ABORSoipZxVzdYgGCgcFWOsELZp0AlbvWlrbA0jJTKCnaorJGDsnzL7cNsNb76DuIJx3A63MR4kpD7KdvUWPdukrZKjfO5g6xAgV5bRw3vO8Qe9bkl2VjSCmi--dxps-m9Y9pfTate9PyG1SwdUk
ContentType Journal Article
DBID AAYXX
CITATION
DOA
DOI 10.25195/ijci.v50i2.502
DatabaseName CrossRef
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
DatabaseTitleList
CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 2520-4912
EndPage 206
ExternalDocumentID oai_doaj_org_article_e9df85c84bfc4fee8a5a49bf1b184bee
10_25195_ijci_v50i2_502
GroupedDBID .K5
AAYXX
ADBBV
ALMA_UNASSIGNED_HOLDINGS
BCNDV
CITATION
GROUPED_DOAJ
ID FETCH-LOGICAL-c1472-957b24ea02a433e57080f9de6a20abed3f2160cf2a54fdc7c6e7c0b6532d68cb3
IEDL.DBID DOA
ISSN 2313-190X
IngestDate Fri Oct 03 12:51:20 EDT 2025
Sat Nov 29 08:12:57 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Language Arabic
English
License http://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c1472-957b24ea02a433e57080f9de6a20abed3f2160cf2a54fdc7c6e7c0b6532d68cb3
OpenAccessLink https://doaj.org/article/e9df85c84bfc4fee8a5a49bf1b184bee
PageCount 21
ParticipantIDs doaj_primary_oai_doaj_org_article_e9df85c84bfc4fee8a5a49bf1b184bee
crossref_primary_10_25195_ijci_v50i2_502
PublicationCentury 2000
PublicationDate 2024-12-30
PublicationDateYYYYMMDD 2024-12-30
PublicationDate_xml – month: 12
  year: 2024
  text: 2024-12-30
  day: 30
PublicationDecade 2020
PublicationTitle Iraqi journal for computers and informatics (Online)
PublicationYear 2024
Publisher University of Information Technology and Communications
Publisher_xml – name: University of Information Technology and Communications
SSID ssib033856039
ssib050733108
ssj0002246376
Score 2.2785616
Snippet Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing...
SourceID doaj
crossref
SourceType Open Website
Index Database
StartPage 186
SubjectTerms acute lymphoblastic leukemia, convolutional autoencoder, convolutional neural network, feature extraction, computer-aided diagnosis
Title Computer-Aided Diagnosis of Acute Lymphoblastic Leukemiaby Using a Novel CAE-CNN Framework
URI https://doaj.org/article/e9df85c84bfc4fee8a5a49bf1b184bee
Volume 50
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 2520-4912
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002246376
  issn: 2313-190X
  databaseCode: DOA
  dateStart: 20140101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2520-4912
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssib050733108
  issn: 2313-190X
  databaseCode: M~E
  dateStart: 20140101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09b9swECWKIEOXpmlTNG1acOjQhYnED0kcXddGB1fo0ABGFuFIHgGlSRw4iQEv_e05UVLqLUsXDuSBEN6Rujvh9B5jXxRWmQ0ORVWgEzpoJyCGQNfd6iBzKG2EJDZR1nW1XNpfO1JfXU9YTw_cA3eGNsTK-Eq76HVErMCAti7mjmoTh9i9fbPS7hRTdJKo7qJI_o_v0iRpwoHF5jKRvuhCDcpzuRIUFZc970_3I6c5ay99e7oxWStPzfDBZQxZO8z-KQTNX7NXQ-7IJ_0zH7IXsH7DDkZdBj5c07fsYpwSkzZg4N_7drr2jq8in3ha4YsteXHlKHWmvfgCH_7gdQtuy1MLAQderzZ4xaeTmZjWNZ-PLVxH7Hw--z39IQYNBeFzXUphTemkRsgkaKXQlJQhRhuwAJmBw6CizIvMRwlGx-BLX2DpM1cYJUNReafesb2b1Q2-ZxyCVUoD5AFRk7nTGipnjZeFD9bCMfs6wtTc9lQZDZUYCdGmQ7RJiDaE6DH71sH4ZNZxXKcJ8nwzeL55zvMf_scmH9lLSWlKom7MTtje_foBP7F9v7lv79af06Gi8eff2SMbI9Kz
linkProvider Directory of Open Access Journals
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Computer-Aided+Diagnosis+of+Acute+Lymphoblastic+Leukemiaby+Using+a+Novel+CAE-CNN+Framework&rft.jtitle=Iraqi+journal+for+computers+and+informatics+%28Online%29&rft.au=Mansoor+Alhammadi%2C+Mohammed&rft.date=2024-12-30&rft.issn=2313-190X&rft.eissn=2520-4912&rft.volume=50&rft.issue=2&rft.spage=186&rft.epage=206&rft_id=info:doi/10.25195%2Fijci.v50i2.502&rft.externalDBID=n%2Fa&rft.externalDocID=10_25195_ijci_v50i2_502
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2313-190X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2313-190X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2313-190X&client=summon