DMRBNet: Dilated Multi-scale Residual Block-based Deep Network for Detection of Breast Cancer from MRI Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149573" target="_blank" >RIV/00216305:26220/23:PU149573 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

DMRBNet: Dilated Multi-scale Residual Block-based Deep Network for Detection of Breast Cancer from MRI Images

Popis výsledku v původním jazyce

Breast cancer (BC) is a common type of cancer that develops from breast tissue cells. Early detection is critical, and mammography is an important tool for this. A biopsy is indicated for lesions with a risk of malignancy of more than 2%, however, only a tiny number of them are confirmed to be malignant. Magnetic Resonance Imaging (MRI) is employed to eliminateunneeded biopsies, but it is a sophisticated and time-consuming operation requiring specialized knowledge. To improve breast cancer diagnosis, a computer-aided diagnostic system using MRI images was developed. The system utilizes a novel neural network called dilated multi-scale residual block-based convolutional neural network (DMRBNet), which effectively extracts features from various image regions. Compared to seven recent advanced approaches, DMRBNet demonstrated superior performance on the BC-MRI dataset. The accuracy of the network is 98.57%, and the error rate is 0.1005. These findings highlight its potential for medical and industrial applications in breast cancer detection.

Název v anglickém jazyce

DMRBNet: Dilated Multi-scale Residual Block-based Deep Network for Detection of Breast Cancer from MRI Images

Popis výsledku anglicky

Breast cancer (BC) is a common type of cancer that develops from breast tissue cells. Early detection is critical, and mammography is an important tool for this. A biopsy is indicated for lesions with a risk of malignancy of more than 2%, however, only a tiny number of them are confirmed to be malignant. Magnetic Resonance Imaging (MRI) is employed to eliminateunneeded biopsies, but it is a sophisticated and time-consuming operation requiring specialized knowledge. To improve breast cancer diagnosis, a computer-aided diagnostic system using MRI images was developed. The system utilizes a novel neural network called dilated multi-scale residual block-based convolutional neural network (DMRBNet), which effectively extracts features from various image regions. Compared to seven recent advanced approaches, DMRBNet demonstrated superior performance on the BC-MRI dataset. The accuracy of the network is 98.57%, and the error rate is 0.1005. These findings highlight its potential for medical and industrial applications in breast cancer detection.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/VK01010153" target="_blank" >VK01010153: Vývoj umělé inteligence pro systém multimodální nedestruktivní forenzní analýzy materiálů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

38-43

Název nakladatele

Neuveden

Místo vydání

Ghent

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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