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A systematic review of breast cancer detection on thermal images

Communications in Science and Technology · Vol. 8 No. 2 (2023) · hlm. 216–225

DOI
10.21924/cst.8.2.2023.1270
ISSN
2502-9266, 2502-9258
Terbit
2023-12-31
Penerbit
Komunitas Ilmuwan dan Profesional Muslim Indonesia
Bahasa
eng

Abstrak

Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.

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34 penulis · 79 tautan kepenulisan bersama · 22 lintas organisasi

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  • Universitas Gadjah Mada
  • Telkom University
  • Muhammadiyah University Purwokerto
  • Universitas Islam Negeri Imam Bonjol Padang
  • Sunan Gunung Djati State Islamic University Bandung
  • State University of Semarang
  • Institut Teknologi Telkom Purwokerto
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Rekan penulisOrganisasiArtikel bersama
Igi ArdiyantoUniversitas Gadjah Mada3
Sunu WibiramaUniversitas Gadjah Mada3
Noor Akhmad SetiawanUniversitas Gadjah Mada3
Widhia K.Z. OktoeberzaUniversitas Gadjah Mada3
Achmad RizalTelkom University2
Risanuri HidayatUniversitas Gadjah Mada2
Lina ChoridahUniversitas Gadjah Mada2
Aulia ArhamUniversitas Islam Negeri Imam Bonjol Padang2
Christianus Frederick HotamaUniversitas Gadjah Mada2
Medycha EmhandyksaUniversitas Gadjah Mada1
Rina SusilowatiUniversitas Gadjah Mada1
Willy Anugrah CahyadiTelkom University1
Tri Putri LestariSunan Gunung Djati State Islamic University Bandung1
Rezty Amalia ArasUniversitas Gadjah Mada1
Hasnely HasnelyAfiliasi tidak diketahui1
Budi WindartaUniversitas Gadjah Mada1
Anan NugrohoState University of Semarang1
Latifah ListyalinaUniversitas Gadjah Mada1
Oyas WahyunggoroUniversitas Gadjah Mada1
Herryawan PujiharsonoInstitut Teknologi Telkom Purwokerto1
Faza Maula AzifUniversitas Gadjah Mada1
Febry Putra RochimUniversitas Gadjah Mada1
Chandra Ramadhan Atmaja PerdanaUniversitas Gadjah Mada1
Syukron Abu Ishaq AlfaroziUniversitas Gadjah Mada1
Etik IrijantiMuhammadiyah University of Yogyakarta1
Tole SutiknoUniversitas Ahmad Dahlan1
Sekar SariUniversitas Gadjah Mada1
Teguh Bharata AdjiUniversitas Gadjah Mada1
Fauziazzuhry RahadianUniversitas Gadjah Mada1
Ratna Lestari Budiani BuanaUniversitas Gadjah Mada1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://cst.kipmi.or.id/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A systematic review of breast cancer detection on thermal images</dc:title>
	<dc:creator>Aqil Aqthobirrobbany</dc:creator>
	<dc:creator>Dian Nova Kusuma Hardani</dc:creator>
	<dc:creator>Indah Soesanti</dc:creator>
	<dc:creator>Adi Nugroho, Hanung</dc:creator>
	<dc:subject xml:lang="en-US">Artificial intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Breast cancer</dc:subject>
	<dc:subject xml:lang="en-US">Deep learning</dc:subject>
	<dc:subject xml:lang="en-US">Systematic literature review</dc:subject>
	<dc:subject xml:lang="en-US">Thermal breast images</dc:subject>
	<dc:description xml:lang="en-US">Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.</dc:description>
	<dc:publisher xml:lang="en-US">Komunitas Ilmuwan dan Profesional Muslim Indonesia</dc:publisher>
	<dc:date>2023-12-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://cst.kipmi.or.id/journal/article/view/1270</dc:identifier>
	<dc:identifier>10.21924/cst.8.2.2023.1270</dc:identifier>
	<dc:source xml:lang="en-US">Communications in Science and Technology; Vol. 8 No. 2 (2023); 216-225</dc:source>
	<dc:source xml:lang="id-ID">Communications in Science and Technology; Vol 8 No 2 (2023); 216-225</dc:source>
	<dc:source>2502-9266</dc:source>
	<dc:source>2502-9258</dc:source>
	<dc:source>10.21924/cst.8.2.2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://cst.kipmi.or.id/journal/article/view/1270/150</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Communications in Science and Technology</dc:rights>
</oai_dc:dc>