Imperial College London
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ProfessorArnabMajumdar

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor of Transport Risk and Safety
 
 
 
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Contact

 

+44 (0)20 7594 6037a.majumdar

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

604Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Singh:2023:10.32604/cmc.2023.032363,
author = {Singh, S and Singh, Rawat S and Gupta, M and K, Tripathi B and Alanzi, F and Majumdar, A and Khuwuthyakorn, P and Thinnukool, O},
doi = {10.32604/cmc.2023.032363},
journal = {Computers, Materials and Continua},
pages = {3063--3083},
title = {Hybrid models for breast cancer detection via transfer learning technique},
url = {http://dx.doi.org/10.32604/cmc.2023.032363},
volume = {74},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost. Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper. The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images.
AU  - Singh,S
AU  - Singh,Rawat S
AU  - Gupta,M
AU  - K,Tripathi B
AU  - Alanzi,F
AU  - Majumdar,A
AU  - Khuwuthyakorn,P
AU  - Thinnukool,O
DO  - 10.32604/cmc.2023.032363
EP  - 3083
PY  - 2023///
SN  - 1546-2218
SP  - 3063
TI  - Hybrid models for breast cancer detection via transfer learning technique
T2  - Computers, Materials and Continua
UR  - http://dx.doi.org/10.32604/cmc.2023.032363
UR  - https://www.techscience.com/cmc/v74n2/50245
UR  - http://hdl.handle.net/10044/1/101264
VL  - 74
ER  -
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