Optimized convolutional neural network model for multilevel classification in leukemia diagnosis using Tversky loss Article Swipe

View

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.36922/aih.4710

Leukemia diagnosis traditionally depends on time-intensive examination of blood cell morphology, a process prone to human error. To address these challenges, this study explores the use of convolutional neural networks (CNNs) optimized with the Tversky loss function for automated, multilevel image classification in leukemia diagnostics. The model was designed to tackle binary classification for distinguishing normal from abnormal cells, and multiclass classification for identifying leukemia subtypes, while addressing the challenges of imbalanced datasets inherent in medical imaging. Trained on publicly available leukemia image datasets, the CNN achieved high accuracy in both tasks, effectively capturing subtle morphological variations critical for precise diagnosis. By incorporating performance metrics such as accuracy, precision, and recall, the study highlights the model’s reliability and robustness across classification tasks. The findings underscore the potential of CNN-based tools in enhancing diagnostic accuracy and efficiency, paving the way for future innovations in leukemia diagnostics and broader medical imaging applications.

Related Topics

Convolutional Neural Network

Computer Science

Artificial Intelligence

Concepts

Convolutional neural network Computer science Artificial intelligence Pattern recognition (psychology)

Metadata

Type: article
Language: en
Landing Page: https://doi.org/10.36922/aih.4710
OA Status: hybrid
Related Works: 10
OpenAlex ID: https://openalex.org/W4406772432

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4406772432

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.36922/aih.4710

Digital Object Identifier
Title: Optimized convolutional neural network model for multilevel classification in leukemia diagnosis using Tversky loss

Work title
Type: article

OpenAlex work type
Language: en

Primary language
Publication year: 2025

Year of publication
Publication date: 2025-01-22

Full publication date if available
Authors: Kumari Pritee, Rahul Garg

List of authors in order
Landing page: https://doi.org/10.36922/aih.4710

Publisher landing page
Open access: Yes

Whether a free full text is available
OA status: hybrid

Open access status per OpenAlex
OA URL: https://doi.org/10.36922/aih.4710

Direct OA link when available
Concepts: Convolutional neural network, Computer science, Artificial intelligence, Pattern recognition (psychology)

Top concepts (fields/topics) attached by OpenAlex
Cited by: 0

Total citation count in OpenAlex
Related works (count): 10

Other works algorithmically related by OpenAlex

Full payload

id	https://openalex.org/W4406772432
doi	https://doi.org/10.36922/aih.4710
ids.doi	https://doi.org/10.36922/aih.4710
ids.openalex	https://openalex.org/W4406772432
fwci	0.0
type	article
title	Optimized convolutional neural network model for multilevel classification in leukemia diagnosis using Tversky loss
biblio.issue	3
biblio.volume	2
biblio.last_page	63
biblio.first_page	63
topics[0].id	https://openalex.org/T12874
topics[0].field.id	https://openalex.org/fields/17
topics[0].field.display_name	Computer Science
topics[0].score	0.9527000188827515
topics[0].domain.id	https://openalex.org/domains/3
topics[0].domain.display_name	Physical Sciences
topics[0].subfield.id	https://openalex.org/subfields/1707
topics[0].subfield.display_name	Computer Vision and Pattern Recognition
topics[0].display_name	Digital Imaging for Blood Diseases
is_xpac	False
apc_list
apc_paid
concepts[0].id	https://openalex.org/C81363708
concepts[0].level	2
concepts[0].score	0.6168510913848877
concepts[0].wikidata	https://www.wikidata.org/wiki/Q17084460
concepts[0].display_name	Convolutional neural network
concepts[1].id	https://openalex.org/C41008148
concepts[1].level	0
concepts[1].score	0.5847033262252808
concepts[1].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[1].display_name	Computer science
concepts[2].id	https://openalex.org/C154945302
concepts[2].level	1
concepts[2].score	0.44872257113456726
concepts[2].wikidata	https://www.wikidata.org/wiki/Q11660
concepts[2].display_name	Artificial intelligence
concepts[3].id	https://openalex.org/C153180895
concepts[3].level	2
concepts[3].score	0.3625534474849701
concepts[3].wikidata	https://www.wikidata.org/wiki/Q7148389
concepts[3].display_name	Pattern recognition (psychology)
keywords[0].id	https://openalex.org/keywords/convolutional-neural-network
keywords[0].score	0.6168510913848877
keywords[0].display_name	Convolutional neural network
keywords[1].id	https://openalex.org/keywords/computer-science
keywords[1].score	0.5847033262252808
keywords[1].display_name	Computer science
keywords[2].id	https://openalex.org/keywords/artificial-intelligence
keywords[2].score	0.44872257113456726
keywords[2].display_name	Artificial intelligence
keywords[3].id	https://openalex.org/keywords/pattern-recognition
keywords[3].score	0.3625534474849701
keywords[3].display_name	Pattern recognition (psychology)
language	en
locations[0].id	doi:10.36922/aih.4710
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S5407045065
locations[0].source.issn	3029-2387, 3041-0894
locations[0].source.type	journal
locations[0].source.is_oa	False
locations[0].source.issn_l	3029-2387
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	Artificial Intelligence in Health
locations[0].source.host_organization
locations[0].source.host_organization_name
locations[0].license	cc-by
locations[0].pdf_url
locations[0].version	publishedVersion
locations[0].raw_type	journal-article
locations[0].license_id	https://openalex.org/licenses/cc-by
locations[0].is_accepted	True
locations[0].is_published	True
locations[0].raw_source_name	Artificial Intelligence in Health
locations[0].landing_page_url	https://doi.org/10.36922/aih.4710
indexed_in	crossref
authorships[0].author.id	https://openalex.org/A5116010309
authorships[0].author.orcid
authorships[0].author.display_name	Kumari Pritee
authorships[0].author_position	first
authorships[0].raw_author_name	Kumari Pritee
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5024268264
authorships[1].author.orcid	https://orcid.org/0000-0003-3684-8962
authorships[1].author.display_name	Rahul Garg
authorships[1].author_position	last
authorships[1].raw_author_name	Rahul Dev Garg
authorships[1].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://doi.org/10.36922/aih.4710
open_access.oa_status	hybrid
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	Optimized convolutional neural network model for multilevel classification in leukemia diagnosis using Tversky loss
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T03:46:38.306776
primary_topic.id	https://openalex.org/T12874
primary_topic.field.id	https://openalex.org/fields/17
primary_topic.field.display_name	Computer Science
primary_topic.score	0.9527000188827515
primary_topic.domain.id	https://openalex.org/domains/3
primary_topic.domain.display_name	Physical Sciences
primary_topic.subfield.id	https://openalex.org/subfields/1707
primary_topic.subfield.display_name	Computer Vision and Pattern Recognition
primary_topic.display_name	Digital Imaging for Blood Diseases
related_works	https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W4391913857, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2033914206, https://openalex.org/W2042327336
cited_by_count	0
locations_count	1
best_oa_location.id	doi:10.36922/aih.4710
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S5407045065
best_oa_location.source.issn	3029-2387, 3041-0894
best_oa_location.source.type	journal
best_oa_location.source.is_oa	False
best_oa_location.source.issn_l	3029-2387
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	Artificial Intelligence in Health
best_oa_location.source.host_organization
best_oa_location.source.host_organization_name
best_oa_location.license	cc-by
best_oa_location.pdf_url
best_oa_location.version	publishedVersion
best_oa_location.raw_type	journal-article
best_oa_location.license_id	https://openalex.org/licenses/cc-by
best_oa_location.is_accepted	True
best_oa_location.is_published	True
best_oa_location.raw_source_name	Artificial Intelligence in Health
best_oa_location.landing_page_url	https://doi.org/10.36922/aih.4710
primary_location.id	doi:10.36922/aih.4710
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S5407045065
primary_location.source.issn	3029-2387, 3041-0894
primary_location.source.type	journal
primary_location.source.is_oa	False
primary_location.source.issn_l	3029-2387
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	Artificial Intelligence in Health
primary_location.source.host_organization
primary_location.source.host_organization_name
primary_location.license	cc-by
primary_location.pdf_url
primary_location.version	publishedVersion
primary_location.raw_type	journal-article
primary_location.license_id	https://openalex.org/licenses/cc-by
primary_location.is_accepted	True
primary_location.is_published	True
primary_location.raw_source_name	Artificial Intelligence in Health
primary_location.landing_page_url	https://doi.org/10.36922/aih.4710
publication_date	2025-01-22
publication_year	2025
referenced_works_count	0
abstract_inverted_index.a	11
abstract_inverted_index.By	101
abstract_inverted_index.To	17
abstract_inverted_index.as	106
abstract_inverted_index.in	42, 74, 89, 130, 142
abstract_inverted_index.of	7, 26, 70, 127
abstract_inverted_index.on	4, 78
abstract_inverted_index.to	14, 49
abstract_inverted_index.CNN	85
abstract_inverted_index.The	45, 122
abstract_inverted_index.and	59, 109, 117, 134, 145
abstract_inverted_index.for	37, 53, 62, 98, 139
abstract_inverted_index.the	24, 33, 68, 84, 111, 114, 125, 137
abstract_inverted_index.use	25
abstract_inverted_index.was	47
abstract_inverted_index.way	138
abstract_inverted_index.both	90
abstract_inverted_index.cell	9
abstract_inverted_index.from	56
abstract_inverted_index.high	87
abstract_inverted_index.loss	35
abstract_inverted_index.such	105
abstract_inverted_index.this	21
abstract_inverted_index.with	32
abstract_inverted_index.blood	8
abstract_inverted_index.human	15
abstract_inverted_index.image	40, 82
abstract_inverted_index.model	46
abstract_inverted_index.prone	13
abstract_inverted_index.study	22, 112
abstract_inverted_index.these	19
abstract_inverted_index.tools	129
abstract_inverted_index.while	66
abstract_inverted_index.(CNNs)	30
abstract_inverted_index.across	119
abstract_inverted_index.binary	51
abstract_inverted_index.cells,	58
abstract_inverted_index.error.	16
abstract_inverted_index.future	140
abstract_inverted_index.neural	28
abstract_inverted_index.normal	55
abstract_inverted_index.paving	136
abstract_inverted_index.subtle	94
abstract_inverted_index.tackle	50
abstract_inverted_index.tasks,	91
abstract_inverted_index.tasks.	121
abstract_inverted_index.Trained	77
abstract_inverted_index.Tversky	34
abstract_inverted_index.address	18
abstract_inverted_index.broader	146
abstract_inverted_index.depends	3
abstract_inverted_index.imaging	148
abstract_inverted_index.medical	75, 147
abstract_inverted_index.metrics	104
abstract_inverted_index.precise	99
abstract_inverted_index.process	12
abstract_inverted_index.recall,	110
abstract_inverted_index.Leukemia	0
abstract_inverted_index.abnormal	57
abstract_inverted_index.accuracy	88, 133
abstract_inverted_index.achieved	86
abstract_inverted_index.critical	97
abstract_inverted_index.datasets	72
abstract_inverted_index.designed	48
abstract_inverted_index.explores	23
abstract_inverted_index.findings	123
abstract_inverted_index.function	36
abstract_inverted_index.imaging.	76
abstract_inverted_index.inherent	73
abstract_inverted_index.leukemia	43, 64, 81, 143
abstract_inverted_index.networks	29
abstract_inverted_index.publicly	79
abstract_inverted_index.CNN-based	128
abstract_inverted_index.accuracy,	107
abstract_inverted_index.available	80
abstract_inverted_index.capturing	93
abstract_inverted_index.datasets,	83
abstract_inverted_index.diagnosis	1
abstract_inverted_index.enhancing	131
abstract_inverted_index.optimized	31
abstract_inverted_index.potential	126
abstract_inverted_index.subtypes,	65
abstract_inverted_index.addressing	67
abstract_inverted_index.automated,	38
abstract_inverted_index.challenges	69
abstract_inverted_index.diagnosis.	100
abstract_inverted_index.diagnostic	132
abstract_inverted_index.highlights	113
abstract_inverted_index.imbalanced	71
abstract_inverted_index.multiclass	60
abstract_inverted_index.multilevel	39
abstract_inverted_index.precision,	108
abstract_inverted_index.robustness	118
abstract_inverted_index.underscore	124
abstract_inverted_index.variations	96
abstract_inverted_index.challenges,	20
abstract_inverted_index.diagnostics	144
abstract_inverted_index.effectively	92
abstract_inverted_index.efficiency,	135
abstract_inverted_index.examination	6
abstract_inverted_index.identifying	63
abstract_inverted_index.innovations	141
abstract_inverted_index.morphology,	10
abstract_inverted_index.performance	103
abstract_inverted_index.reliability	116
abstract_inverted_index.diagnostics.	44
abstract_inverted_index.applications.	149
abstract_inverted_index.convolutional	27
abstract_inverted_index.incorporating	102
abstract_inverted_index.morphological	95
abstract_inverted_index.traditionally	2
abstract_inverted_index.classification	41, 52, 61, 120
abstract_inverted_index.distinguishing	54
abstract_inverted_index.time-intensive	5
abstract_inverted_index.model&rsquo;s	115
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	2
citation_normalized_percentile.value	0.01711347
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False