Brain Tumor Segmentation and Classification from MRI Images using Improved FLICM Segmentation and SCA Weight Optimized Wavelet-ELM Model Article Swipe
Debendra Kumar Sahoo
,
Satyasis Mishra
,
Mihir Narayan Mohanty
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0130753
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0130753
Image segmentation is an essential technique of brain tumor MRI image processing for automated diagnosis of an image by partitioning it into distinct regions referred to as a set of pixels. The classification of the tumor affected and non-tumor becomes an arduous task for radiologists. This paper presents a novel image enhancement based on the SCA (Sine Cosine Algorithm) optimization technique for the improvement of image quality. The improved FLICM (Fuzzy Local Information C Means) segmentation technique is proposed to detect the affected regions of brain tumor from the MRI brain tumor images and reduction of noise from the MRI images by introducing a fuzzy factor to the objective function. The SCA weight-optimized Wavelet-Extreme Learning Machine (SCA-WELM) model is also proposed for the classification of benign tumors and malignant tumors from MRI brain images. In the first instance, the enhanced images are undergone improved FLICM Segmentation. In the second phase, the segmented images are utilized for feature extraction. The GLCM feature extraction technique is considered for feature extraction. The extracted features are aligned as input to the SCA-WELM model for the classification of benign and malignant tumors. The following dataset (Dataset-255) is considered for evaluating the proposed classification approach. An accuracy of 99.12% is achieved by the improved FLICM segmentation technique. The classification performance of the SCA-WELM is measured by sensitivity, specificity, accuracy, and computational time and achieved 0.98, 0.99, 99.21%, and 97.2576 seconds respectively. The comparison results of SVM (Support Vector Machine), ELM, SCA-ELM, and proposed SCA-WELM models are presented to show the robustness of the proposed SCA-WELM classification model.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2022.0130753
- http://thesai.org/Downloads/Volume13No7/Paper_53-Brain_Tumor_Segmentation_and_Classification.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289865933
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4289865933Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2022.0130753Digital Object Identifier
- Title
-
Brain Tumor Segmentation and Classification from MRI Images using Improved FLICM Segmentation and SCA Weight Optimized Wavelet-ELM ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Debendra Kumar Sahoo, Satyasis Mishra, Mihir Narayan MohantyList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2022.0130753Publisher landing page
- PDF URL
-
https://thesai.org/Downloads/Volume13No7/Paper_53-Brain_Tumor_Segmentation_and_Classification.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume13No7/Paper_53-Brain_Tumor_Segmentation_and_Classification.pdfDirect OA link when available
- Concepts
-
Artificial intelligence, Computer science, Pattern recognition (psychology), Support vector machine, Segmentation, Feature extraction, Image segmentation, Feature (linguistics), Pixel, Wavelet, Computer vision, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 5Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4289865933 |
|---|---|
| doi | https://doi.org/10.14569/ijacsa.2022.0130753 |
| ids.doi | https://doi.org/10.14569/ijacsa.2022.0130753 |
| ids.openalex | https://openalex.org/W4289865933 |
| fwci | 0.67282258 |
| type | article |
| title | Brain Tumor Segmentation and Classification from MRI Images using Improved FLICM Segmentation and SCA Weight Optimized Wavelet-ELM Model |
| biblio.issue | 7 |
| biblio.volume | 13 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12702 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9991000294685364 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2808 |
| topics[0].subfield.display_name | Neurology |
| topics[0].display_name | Brain Tumor Detection and Classification |
| topics[1].id | https://openalex.org/T12676 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9980000257492065 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Machine Learning and ELM |
| topics[2].id | https://openalex.org/T10036 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9672999978065491 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Advanced Neural Network Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.8084792494773865 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7912923097610474 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.675356388092041 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C12267149 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6309816241264343 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[3].display_name | Support vector machine |
| concepts[4].id | https://openalex.org/C89600930 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6111564636230469 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[4].display_name | Segmentation |
| concepts[5].id | https://openalex.org/C52622490 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5132355690002441 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1026626 |
| concepts[5].display_name | Feature extraction |
| concepts[6].id | https://openalex.org/C124504099 |
| concepts[6].level | 3 |
| concepts[6].score | 0.474604070186615 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[6].display_name | Image segmentation |
| concepts[7].id | https://openalex.org/C2776401178 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4608153700828552 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[7].display_name | Feature (linguistics) |
| concepts[8].id | https://openalex.org/C160633673 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4169095456600189 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q355198 |
| concepts[8].display_name | Pixel |
| concepts[9].id | https://openalex.org/C47432892 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4132641553878784 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q831390 |
| concepts[9].display_name | Wavelet |
| concepts[10].id | https://openalex.org/C31972630 |
| concepts[10].level | 1 |
| concepts[10].score | 0.35155799984931946 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[10].display_name | Computer vision |
| concepts[11].id | https://openalex.org/C41895202 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[11].display_name | Linguistics |
| concepts[12].id | https://openalex.org/C138885662 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[12].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.8084792494773865 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7912923097610474 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.675356388092041 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/support-vector-machine |
| keywords[3].score | 0.6309816241264343 |
| keywords[3].display_name | Support vector machine |
| keywords[4].id | https://openalex.org/keywords/segmentation |
| keywords[4].score | 0.6111564636230469 |
| keywords[4].display_name | Segmentation |
| keywords[5].id | https://openalex.org/keywords/feature-extraction |
| keywords[5].score | 0.5132355690002441 |
| keywords[5].display_name | Feature extraction |
| keywords[6].id | https://openalex.org/keywords/image-segmentation |
| keywords[6].score | 0.474604070186615 |
| keywords[6].display_name | Image segmentation |
| keywords[7].id | https://openalex.org/keywords/feature |
| keywords[7].score | 0.4608153700828552 |
| keywords[7].display_name | Feature (linguistics) |
| keywords[8].id | https://openalex.org/keywords/pixel |
| keywords[8].score | 0.4169095456600189 |
| keywords[8].display_name | Pixel |
| keywords[9].id | https://openalex.org/keywords/wavelet |
| keywords[9].score | 0.4132641553878784 |
| keywords[9].display_name | Wavelet |
| keywords[10].id | https://openalex.org/keywords/computer-vision |
| keywords[10].score | 0.35155799984931946 |
| keywords[10].display_name | Computer vision |
| language | en |
| locations[0].id | doi:10.14569/ijacsa.2022.0130753 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S23629721 |
| locations[0].source.issn | 2156-5570, 2158-107X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2156-5570 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal of Advanced Computer Science and Applications |
| locations[0].source.host_organization | https://openalex.org/P4310311819 |
| locations[0].source.host_organization_name | Science and Information Organization |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311819 |
| locations[0].source.host_organization_lineage_names | Science and Information Organization |
| locations[0].license | cc-by |
| locations[0].pdf_url | http://thesai.org/Downloads/Volume13No7/Paper_53-Brain_Tumor_Segmentation_and_Classification.pdf |
| 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 | International Journal of Advanced Computer Science and Applications |
| locations[0].landing_page_url | https://doi.org/10.14569/ijacsa.2022.0130753 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5014774114 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-3047-8630 |
| authorships[0].author.display_name | Debendra Kumar Sahoo |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I193073490 |
| authorships[0].affiliations[0].raw_affiliation_string | Dept. of ECE SOA University Odisha, India |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I47639616 |
| authorships[0].affiliations[1].raw_affiliation_string | Dept. of ECE Centurion University of Technology and Management, Bhubaneswar Odisha, India |
| authorships[0].institutions[0].id | https://openalex.org/I47639616 |
| authorships[0].institutions[0].ror | https://ror.org/03js1g511 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I47639616 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Centurion University of Technology and Management |
| authorships[0].institutions[1].id | https://openalex.org/I193073490 |
| authorships[0].institutions[1].ror | https://ror.org/056ep7w45 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I193073490 |
| authorships[0].institutions[1].country_code | IN |
| authorships[0].institutions[1].display_name | Siksha O Anusandhan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Debendra Kumar Sahoo |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Dept. of ECE Centurion University of Technology and Management, Bhubaneswar Odisha, India, Dept. of ECE SOA University Odisha, India |
| authorships[1].author.id | https://openalex.org/A5100709970 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3515-4467 |
| authorships[1].author.display_name | Satyasis Mishra |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I47639616 |
| authorships[1].affiliations[0].raw_affiliation_string | Dept. of ECE Centurion University of Technology and Management, Bhubaneswar Odisha, India |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I193073490 |
| authorships[1].affiliations[1].raw_affiliation_string | Dept. of ECE SOA University Odisha, India |
| authorships[1].institutions[0].id | https://openalex.org/I47639616 |
| authorships[1].institutions[0].ror | https://ror.org/03js1g511 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I47639616 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Centurion University of Technology and Management |
| authorships[1].institutions[1].id | https://openalex.org/I193073490 |
| authorships[1].institutions[1].ror | https://ror.org/056ep7w45 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I193073490 |
| authorships[1].institutions[1].country_code | IN |
| authorships[1].institutions[1].display_name | Siksha O Anusandhan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Satyasis Mishra |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Dept. of ECE Centurion University of Technology and Management, Bhubaneswar Odisha, India, Dept. of ECE SOA University Odisha, India |
| authorships[2].author.id | https://openalex.org/A5016209473 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1252-949X |
| authorships[2].author.display_name | Mihir Narayan Mohanty |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Mihir Narayan Mohanty |
| authorships[2].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | http://thesai.org/Downloads/Volume13No7/Paper_53-Brain_Tumor_Segmentation_and_Classification.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Brain Tumor Segmentation and Classification from MRI Images using Improved FLICM Segmentation and SCA Weight Optimized Wavelet-ELM Model |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12702 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9991000294685364 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2808 |
| primary_topic.subfield.display_name | Neurology |
| primary_topic.display_name | Brain Tumor Detection and Classification |
| related_works | https://openalex.org/W2517104666, https://openalex.org/W2005437358, https://openalex.org/W1669643531, https://openalex.org/W2337415362, https://openalex.org/W2008656436, https://openalex.org/W2134924024, https://openalex.org/W2336974148, https://openalex.org/W2345184372, https://openalex.org/W2185970706, https://openalex.org/W2546942002 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2023 |
| counts_by_year[0].cited_by_count | 5 |
| locations_count | 1 |
| best_oa_location.id | doi:10.14569/ijacsa.2022.0130753 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S23629721 |
| best_oa_location.source.issn | 2156-5570, 2158-107X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2156-5570 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal of Advanced Computer Science and Applications |
| best_oa_location.source.host_organization | https://openalex.org/P4310311819 |
| best_oa_location.source.host_organization_name | Science and Information Organization |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311819 |
| best_oa_location.source.host_organization_lineage_names | Science and Information Organization |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | http://thesai.org/Downloads/Volume13No7/Paper_53-Brain_Tumor_Segmentation_and_Classification.pdf |
| 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 | International Journal of Advanced Computer Science and Applications |
| best_oa_location.landing_page_url | https://doi.org/10.14569/ijacsa.2022.0130753 |
| primary_location.id | doi:10.14569/ijacsa.2022.0130753 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S23629721 |
| primary_location.source.issn | 2156-5570, 2158-107X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2156-5570 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal of Advanced Computer Science and Applications |
| primary_location.source.host_organization | https://openalex.org/P4310311819 |
| primary_location.source.host_organization_name | Science and Information Organization |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311819 |
| primary_location.source.host_organization_lineage_names | Science and Information Organization |
| primary_location.license | cc-by |
| primary_location.pdf_url | http://thesai.org/Downloads/Volume13No7/Paper_53-Brain_Tumor_Segmentation_and_Classification.pdf |
| 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 | International Journal of Advanced Computer Science and Applications |
| primary_location.landing_page_url | https://doi.org/10.14569/ijacsa.2022.0130753 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3175204909, https://openalex.org/W2553444198, https://openalex.org/W2094979888, https://openalex.org/W1550071438, https://openalex.org/W2609856734, https://openalex.org/W2998095093, https://openalex.org/W3160168905, https://openalex.org/W3098365791, https://openalex.org/W2785177491, https://openalex.org/W2963973144, https://openalex.org/W1987140774, https://openalex.org/W3082189421, https://openalex.org/W3198817744, https://openalex.org/W2017896827, https://openalex.org/W1974314515, https://openalex.org/W2049586412, https://openalex.org/W1983851888, https://openalex.org/W2094997308, https://openalex.org/W2893347629, https://openalex.org/W2780099243, https://openalex.org/W2521587260, https://openalex.org/W1985215121, https://openalex.org/W2031223143, https://openalex.org/W1984199710, https://openalex.org/W2044523788, https://openalex.org/W3203733821, https://openalex.org/W4205432032, https://openalex.org/W3164956625, https://openalex.org/W3196946040, https://openalex.org/W2232317135, https://openalex.org/W1589895371, https://openalex.org/W6680142320, https://openalex.org/W2777346020, https://openalex.org/W3135769285, https://openalex.org/W4285525826, https://openalex.org/W2135017002, https://openalex.org/W4224062111, https://openalex.org/W4220724284, https://openalex.org/W2136602355, https://openalex.org/W3027935799, https://openalex.org/W3201949037 |
| referenced_works_count | 41 |
| abstract_inverted_index.C | 73 |
| abstract_inverted_index.a | 27, 48, 103 |
| abstract_inverted_index.An | 199 |
| abstract_inverted_index.In | 134, 146 |
| abstract_inverted_index.an | 3, 16, 40 |
| abstract_inverted_index.as | 26, 173 |
| abstract_inverted_index.by | 18, 101, 205, 219 |
| abstract_inverted_index.is | 2, 77, 118, 163, 191, 203, 217 |
| abstract_inverted_index.it | 20 |
| abstract_inverted_index.of | 6, 15, 29, 33, 64, 84, 95, 124, 182, 201, 214, 238, 255 |
| abstract_inverted_index.on | 53 |
| abstract_inverted_index.to | 25, 79, 106, 175, 251 |
| abstract_inverted_index.MRI | 9, 89, 99, 131 |
| abstract_inverted_index.SCA | 55, 111 |
| abstract_inverted_index.SVM | 239 |
| abstract_inverted_index.The | 31, 67, 110, 158, 168, 187, 211, 235 |
| abstract_inverted_index.and | 37, 93, 127, 184, 223, 226, 231, 245 |
| abstract_inverted_index.are | 141, 153, 171, 249 |
| abstract_inverted_index.for | 12, 43, 61, 121, 155, 165, 179, 193 |
| abstract_inverted_index.set | 28 |
| abstract_inverted_index.the | 34, 54, 62, 81, 88, 98, 107, 122, 135, 138, 147, 150, 176, 180, 195, 206, 215, 253, 256 |
| abstract_inverted_index.ELM, | 243 |
| abstract_inverted_index.GLCM | 159 |
| abstract_inverted_index.This | 45 |
| abstract_inverted_index.also | 119 |
| abstract_inverted_index.from | 87, 97, 130 |
| abstract_inverted_index.into | 21 |
| abstract_inverted_index.show | 252 |
| abstract_inverted_index.task | 42 |
| abstract_inverted_index.time | 225 |
| abstract_inverted_index.(Sine | 56 |
| abstract_inverted_index.0.98, | 228 |
| abstract_inverted_index.0.99, | 229 |
| abstract_inverted_index.FLICM | 69, 144, 208 |
| abstract_inverted_index.Image | 0 |
| abstract_inverted_index.Local | 71 |
| abstract_inverted_index.based | 52 |
| abstract_inverted_index.brain | 7, 85, 90, 132 |
| abstract_inverted_index.first | 136 |
| abstract_inverted_index.fuzzy | 104 |
| abstract_inverted_index.image | 10, 17, 50, 65 |
| abstract_inverted_index.input | 174 |
| abstract_inverted_index.model | 117, 178 |
| abstract_inverted_index.noise | 96 |
| abstract_inverted_index.novel | 49 |
| abstract_inverted_index.paper | 46 |
| abstract_inverted_index.tumor | 8, 35, 86, 91 |
| abstract_inverted_index.(Fuzzy | 70 |
| abstract_inverted_index.99.12% | 202 |
| abstract_inverted_index.Cosine | 57 |
| abstract_inverted_index.Means) | 74 |
| abstract_inverted_index.Vector | 241 |
| abstract_inverted_index.benign | 125, 183 |
| abstract_inverted_index.detect | 80 |
| abstract_inverted_index.factor | 105 |
| abstract_inverted_index.images | 92, 100, 140, 152 |
| abstract_inverted_index.model. | 260 |
| abstract_inverted_index.models | 248 |
| abstract_inverted_index.phase, | 149 |
| abstract_inverted_index.second | 148 |
| abstract_inverted_index.tumors | 126, 129 |
| abstract_inverted_index.97.2576 | 232 |
| abstract_inverted_index.99.21%, | 230 |
| abstract_inverted_index.Machine | 115 |
| abstract_inverted_index.aligned | 172 |
| abstract_inverted_index.arduous | 41 |
| abstract_inverted_index.becomes | 39 |
| abstract_inverted_index.dataset | 189 |
| abstract_inverted_index.feature | 156, 160, 166 |
| abstract_inverted_index.images. | 133 |
| abstract_inverted_index.pixels. | 30 |
| abstract_inverted_index.regions | 23, 83 |
| abstract_inverted_index.results | 237 |
| abstract_inverted_index.seconds | 233 |
| abstract_inverted_index.tumors. | 186 |
| abstract_inverted_index.(Support | 240 |
| abstract_inverted_index.Learning | 114 |
| abstract_inverted_index.SCA-ELM, | 244 |
| abstract_inverted_index.SCA-WELM | 177, 216, 247, 258 |
| abstract_inverted_index.accuracy | 200 |
| abstract_inverted_index.achieved | 204, 227 |
| abstract_inverted_index.affected | 36, 82 |
| abstract_inverted_index.distinct | 22 |
| abstract_inverted_index.enhanced | 139 |
| abstract_inverted_index.features | 170 |
| abstract_inverted_index.improved | 68, 143, 207 |
| abstract_inverted_index.measured | 218 |
| abstract_inverted_index.presents | 47 |
| abstract_inverted_index.proposed | 78, 120, 196, 246, 257 |
| abstract_inverted_index.quality. | 66 |
| abstract_inverted_index.referred | 24 |
| abstract_inverted_index.utilized | 154 |
| abstract_inverted_index.Machine), | 242 |
| abstract_inverted_index.accuracy, | 222 |
| abstract_inverted_index.approach. | 198 |
| abstract_inverted_index.automated | 13 |
| abstract_inverted_index.diagnosis | 14 |
| abstract_inverted_index.essential | 4 |
| abstract_inverted_index.extracted | 169 |
| abstract_inverted_index.following | 188 |
| abstract_inverted_index.function. | 109 |
| abstract_inverted_index.instance, | 137 |
| abstract_inverted_index.malignant | 128, 185 |
| abstract_inverted_index.non-tumor | 38 |
| abstract_inverted_index.objective | 108 |
| abstract_inverted_index.presented | 250 |
| abstract_inverted_index.reduction | 94 |
| abstract_inverted_index.segmented | 151 |
| abstract_inverted_index.technique | 5, 60, 76, 162 |
| abstract_inverted_index.undergone | 142 |
| abstract_inverted_index.(SCA-WELM) | 116 |
| abstract_inverted_index.Algorithm) | 58 |
| abstract_inverted_index.comparison | 236 |
| abstract_inverted_index.considered | 164, 192 |
| abstract_inverted_index.evaluating | 194 |
| abstract_inverted_index.extraction | 161 |
| abstract_inverted_index.processing | 11 |
| abstract_inverted_index.robustness | 254 |
| abstract_inverted_index.technique. | 210 |
| abstract_inverted_index.Information | 72 |
| abstract_inverted_index.enhancement | 51 |
| abstract_inverted_index.extraction. | 157, 167 |
| abstract_inverted_index.improvement | 63 |
| abstract_inverted_index.introducing | 102 |
| abstract_inverted_index.performance | 213 |
| abstract_inverted_index.optimization | 59 |
| abstract_inverted_index.partitioning | 19 |
| abstract_inverted_index.segmentation | 1, 75, 209 |
| abstract_inverted_index.sensitivity, | 220 |
| abstract_inverted_index.specificity, | 221 |
| abstract_inverted_index.(Dataset-255) | 190 |
| abstract_inverted_index.Segmentation. | 145 |
| abstract_inverted_index.computational | 224 |
| abstract_inverted_index.radiologists. | 44 |
| abstract_inverted_index.respectively. | 234 |
| abstract_inverted_index.classification | 32, 123, 181, 197, 212, 259 |
| abstract_inverted_index.Wavelet-Extreme | 113 |
| abstract_inverted_index.weight-optimized | 112 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.62105147 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |