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Convolutional Neural Networks in Medical Image Understanding

Aptisi Transactions on Technopreneurship (ATT) · Vol. 3 No. 2 (2021) · hlm. 120–126

DOI
10.34306/att.v3i2.188
ISSN
2656-8888, 2655-8807
Terbit
2021-09-01
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

In the era of social media images/pictures play a vital role. Facebook, whatsapp, instagram everywhere we see a lot of pictures nowadays. Along with social media, the pictures play a very important role in medical science. Medical Image can help in diagnosis, clinical treatment and teaching tasks. Traditional classification of images has reached an end because of its time taking nature and efforts made to extract, select and classify. This problem is solved with the help of CNN(Convolutional neural network).In medical science we have treatment for body anomalies that were not there before.Using the deep learning models of CNN we can detect the disease like Cancer,Lung Infection and treat it. This article aims to provide a comprehensive survey of applications of CNNs in medical image understanding.

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  • Universitas Raharja
  • Graphic Era Hill University
  • Veer Narmad South Gujarat University
  • Syarif Hidayatullah State Islamic University Jakarta
  • Universitas Catur Insan Cendekia Cirebon
  • Universitas PGRI Palembang
  • Universitas Simalungun
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Bhupesh RawatGraphic Era Hill University3
Tuti NurhaeniUniversitas Raharja2
Nuke Puji Lestari SantosoUniversitas Raharja2
Ihsan Nuril HikamUniversitas Raharja2
Roshan PrakashAfiliasi tidak diketahui2
Kawal AroraAfiliasi tidak diketahui2
Dwi JulianingsihUniversitas Raharja2
Dedeh SupriyantiUniversitas Raharja2
Ninda LutfianiUniversitas Raharja1
Ankur SinghGraphic Era Hill University1
Widya FebrianiUniversitas Raharja1
Husni Teja SukmanaSyarif Hidayatullah State Islamic University Jakarta1
Amitkumar DudhatVeer Narmad South Gujarat University1
Aditya MardiansyahUniversitas Raharja1
Nur AzizahUniversitas Raharja1
Dewi ImmaniarUniversitas Raharja1
Nanda SeptianiUniversitas Raharja1
Aan KaniviaUniversitas Catur Insan Cendekia Cirebon1
Hilda HildaAfiliasi tidak diketahui1
Alfri AdiwijayaUniversitas Raharja1
Muhammad Faizal FazriUniversitas Raharja1
Sabda MaulanaUniversitas Raharja1
Hetilaniar HetilaniarUniversitas PGRI Palembang1
Semaria Eva Elita GirsangUniversitas Simalungun1
Souza Nurafrianto Windiartono PutraSaarland University1
Kawl W KartsUniversity College Dublin1
Kawal AroraAfiliasi tidak diketahui1
Saksham ChaurasiaAfiliasi tidak diketahui1
Saksham ChaurasiaAfiliasi tidak diketahui1
Srishti BiswasAfiliasi tidak diketahui1
Ritesh MishraIFTM University1
Ari AsmawatiUniversitas Raharja1
Prakriti RashiGraphic Era Hill University1
Goh YaoAfiliasi tidak diketahui1

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://att.aptisi.or.id/index.php/att/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Convolutional Neural Networks in Medical Image Understanding</dc:title>
	<dc:creator xml:lang="en">Upreti, Megha</dc:creator>
	<dc:creator xml:lang="en">Pandey, Chitra</dc:creator>
	<dc:creator xml:lang="en">Bist, Ankur Singh</dc:creator>
	<dc:creator xml:lang="en">Rawat, Buphest</dc:creator>
	<dc:creator xml:lang="en">Hardini, Marviola</dc:creator>
	<dc:creator xml:lang="id">Hardini, Marviola</dc:creator>
	<dc:subject xml:lang="en">Feature extraction</dc:subject>
	<dc:subject xml:lang="en">CNN</dc:subject>
	<dc:subject xml:lang="en">Muli-layer Neural Network</dc:subject>
	<dc:subject xml:lang="en">Medical data analysis</dc:subject>
	<dc:description xml:lang="en">In the era of social media images/pictures play a vital&amp;nbsp; role. Facebook, whatsapp, instagram everywhere we see a lot of&amp;nbsp; pictures nowadays. Along with social media, the pictures play a&amp;nbsp; very important role in medical science. Medical Image can help&amp;nbsp; in diagnosis, clinical treatment and teaching tasks. Traditional&amp;nbsp; classification of images has reached an end because of its time&amp;nbsp; taking nature and efforts made to extract, select and classify . This problem is solved with the help of CNN(Convolutional neural network).In medical science we have treatment for body&amp;nbsp; anomalies that were not there before .Using the deep learning&amp;nbsp; models of CNN we can detect the disease like Cancer ,Lung&amp;nbsp; Infection and treat it. This article aims to provide a&amp;nbsp; comprehensive survey of applications of CNNs in medical image&amp;nbsp; understanding.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2021-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://att.aptisi.or.id/index.php/att/article/view/188</dc:identifier>
	<dc:identifier>10.34306/att.v3i2.188</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 3 No. 2 (2021): September; 120-126</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 3 No 2 (2021): September; 120-126</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v3i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/188/147</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 Megha Upreti, Chitra Pandey, Ankur Singh Bist, Buphest Rawat, Marviola Hardini</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>