SANAS-Net: spatial attention neural architecture search for breast cancer detection

D'souza, Melwin; Prabhu Gurpur, Ananth; Kumara, Varuna

doi:10.11591/ijai.v13.i3.pp3339-3349

Akses terbuka Jurnal SINTA 1

SANAS-Net: spatial attention neural architecture search for breast cancer detection

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · hlm. 3339–3349

DOI: 10.11591/ijai.v13.i3.pp3339-3349
ISSN: 2252-8938, 2089-4872
Terbit: 2024-09-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

The utilization of mammography images plays a vital role in the prompt detection and treatment of breast cancer. Breast imaging techniques aid medical professionals in assessing the dimensions, morphology, and spatial orientation of breast lesions, facilitating the differentiation between benign and malignant conditions. Breast tissue can vary widely in terms of density, composition, and structure, leading to complexities in distinguishing between benign and malignant conditions. The primary contribution of this paper is the proposal of a spatial attention-based neural architecture search network (SANAS-Net) technique that incorporates a spatial attention mechanism, enabling the model to learn and prioritize key regions within mammograms (MMs). Multi-head attention is employed within the transformer blocks to effectively capture a wide range of spatial relations and feature interactions. Global contextual information was integrated into the transformer blocks by means of introducing positional embeddings. Several practical studies have been undertaken to verify the effectiveness of our methodology in identifying fully attentive networks that exhibit good performance in distinguishing between malignant and benign breast cancer cases. The experimental study reached a test accuracy of 89.95%, which is way higher than previously proposed algorithms for mammography imagebased breast cancer detection.

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Jain University
Sahyadri College of Engineering & Management
Nitte University
KLS Gogte Institute of Technology
AISSMS Institute of Information Technology
SRM Institute of Science and Technology
M. Kumarasamy College of Engineering
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Rekan penulis	Organisasi	Artikel bersama
Abdul Kareem	Afiliasi tidak diketahui	4
Ezhilarasan Ganesan	Jain University	2
Sanjana Poojary	Afiliasi tidak diketahui	1
T Thilagraj	Afiliasi tidak diketahui	1
Utkrishna Udesh Naik	Afiliasi tidak diketahui	1
R Bharath	Afiliasi tidak diketahui	1
Ranjith Bhat	Jain University	1
Rajashree Nambiar	Nitte University	1
Akshatha Naik	Afiliasi tidak diketahui	1
Ashwini Ashwini	Afiliasi tidak diketahui	1
Navilgone Krishna Vaishnavi	Afiliasi tidak diketahui	1
Ruchitha Kamath Subhashchandra	Afiliasi tidak diketahui	1
Trapthi Trapthi	Afiliasi tidak diketahui	1
Altaf Osman Mulani	AISSMS Institute of Information Technology	1
Swapna Siddamsetti	KLS Gogte Institute of Technology	1
Melwin D Souza	Afiliasi tidak diketahui	1
S Subbaiah	SRM Institute of Science and Technology	1
S. Ramya	M. Kumarasamy College of Engineering	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/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">SANAS-Net: spatial attention neural architecture search for breast cancer detection</dc:title>
	<dc:creator>D'souza, Melwin</dc:creator>
	<dc:creator>Prabhu Gurpur, Ananth</dc:creator>
	<dc:creator>Kumara, Varuna</dc:creator>
	<dc:subject xml:lang="en-US">Neural Network; Machine Learning; Deep Learning</dc:subject>
	<dc:subject xml:lang="en-US">Breast cancer detection; Deep learning; Mammography; Neural architecture search; Spatial attention</dc:subject>
	<dc:description xml:lang="en-US">The utilization of mammography images plays a vital role in the prompt detection and treatment of breast cancer. Breast imaging techniques aid medical professionals in assessing the dimensions, morphology, and spatial orientation of breast lesions, facilitating the differentiation between benign and malignant conditions. Breast tissue can vary widely in terms of density, composition, and structure, leading to complexities in distinguishing between benign and malignant conditions. The primary contribution of this paper is the proposal of a spatial attention-based neural architecture search network (SANAS-Net) technique that incorporates a spatial attention mechanism, enabling the model to learn and prioritize key regions within mammograms (MMs). Multi-head attention is employed within the transformer blocks to effectively capture a wide range of spatial relations and feature interactions. Global contextual information was integrated into the transformer blocks by means of introducing positional embeddings. Several practical studies have been undertaken to verify the effectiveness of our methodology in identifying fully attentive networks that exhibit good performance in distinguishing between malignant and benign breast cancer cases. The experimental study reached a test accuracy of 89.95%, which is way higher than previously proposed algorithms for mammography imagebased breast cancer detection.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Moodlakatte Institute of Technology, Kundapura</dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24632</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp3339-3349</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 3339-3349</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24632/14143</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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