Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging

El trabajo forma parte de la tesis doctoral del primer autor, Dr. Laith Alzubaidi, siendo José Santamaría investigador invitado por el autor del artículo en la co-supervision de dicha tesis doctoral, correspondiendo este con uno de los varios artículos científicos que fueron desarrollados y publicad...

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Autores: Alzubaidi, Laith, Fadhel, Mohammed A., Al-Shamma, Omrad, Zhang, Jinglan, Santamaria, José, Duan, Ye
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/2325
Acceso en línea:https://doi.org/10.1007/s11042-021-10942-9
https://hdl.handle.net/10953/2325
Access Level:acceso abierto
Palabra clave:Medical imaging
Deep learning
Transfer learning
Breast cancer
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spelling Robust Application of New Deep Learning Tools: An Experimental Study in Medical ImagingAlzubaidi, LaithFadhel, Mohammed A.Al-Shamma, OmradZhang, JinglanSantamaria, JoséDuan, YeMedical imagingDeep learningTransfer learningBreast cancerEl trabajo forma parte de la tesis doctoral del primer autor, Dr. Laith Alzubaidi, siendo José Santamaría investigador invitado por el autor del artículo en la co-supervision de dicha tesis doctoral, correspondiendo este con uno de los varios artículos científicos que fueron desarrollados y publicados durante y después de la tesis doctoral del Dr. Alzubaidi.Nowadays medical imaging plays a vital role in diagnosing the various types of diseases among patients across the healthcare system. Robust and accurate analysis of medical data is crucial to achieving a successful diagnosis from physicians. Traditional diagnostic methods are highly time-consuming and prone to handmade errors. Cost is reduced and performance is improved by adopting computer-aided diagnosis methods. Usually, the performance of traditional machine learning (ML) classification methods much depends on both feature extraction and selection methods that are sensitive to colors, shapes, and sizes, which conveys a complex solution when facing classification tasks in medical imaging. Currently, deep learning (DL) tools have become an alternative solution to overcome the drawbacks of traditional methods that make use of handmade features. In this paper, a new DL approach based on a hybrid deep convolutional neural network model is proposed for the automatic classification of several different types of medical images. Specifically, gradient vanishing and over-fitting issues have been properly addressed in the proposed model in order to improve its robustness by means of different tested techniques involving residual links, global average pooling layers, dropout layers, and data augmentation. Additionally, we employed the idea of parallel convolutional layers with the aim of achieving better feature representation by adopting different filter sizes on the same input and then concatenated as a result. The proposed model is trained and tested on the ICIAR 2018 dataset to classify hematoxylin and eosin-stained breast biopsy images into four categories: invasive carcinoma, in situ carcinoma, benign tumors, and normal tissue. As the experimental results show, our proposed method outperforms several of the state-of-the-art methods by achieving rate values of 93.2% and 89.8% for both image- and patch-wise image classification tasks, respectively. Moreover, we fine-tuned our model to classify foot images into two classes in order to test its robustness by considering normal and abnormal diabetic foot ulcer (DFU) image datasets. In this case the model achieved an F1 score value of 94.80% on the public DFU dataset and 97.3% on the private DFU dataset. Lastly, transfer learning (TL) has been adopted to validate the proposed model with multiple classes with the aim of classifying six different wound types. This approach significantly improves the accuracy rate from a rate of 76.92% when trained from scratch to 87.94% when TL was considered. Our proposed model has proven its suitability and robustness by addressing several medical imaging tasks dealing with complex and challenging scenarios.Springer202420242021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1007/s11042-021-10942-9https://hdl.handle.net/10953/2325reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésMultimedia Tools and Applications 2021; 81:13289–13317Atribución-NoComercial-SinDerivadas 3.0 EspañaAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/23252026-06-24T12:41:07Z
dc.title.none.fl_str_mv Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
title Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
spellingShingle Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
Alzubaidi, Laith
Medical imaging
Deep learning
Transfer learning
Breast cancer
title_short Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
title_full Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
title_fullStr Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
title_full_unstemmed Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
title_sort Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging
dc.creator.none.fl_str_mv Alzubaidi, Laith
Fadhel, Mohammed A.
Al-Shamma, Omrad
Zhang, Jinglan
Santamaria, José
Duan, Ye
author Alzubaidi, Laith
author_facet Alzubaidi, Laith
Fadhel, Mohammed A.
Al-Shamma, Omrad
Zhang, Jinglan
Santamaria, José
Duan, Ye
author_role author
author2 Fadhel, Mohammed A.
Al-Shamma, Omrad
Zhang, Jinglan
Santamaria, José
Duan, Ye
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Medical imaging
Deep learning
Transfer learning
Breast cancer
topic Medical imaging
Deep learning
Transfer learning
Breast cancer
description El trabajo forma parte de la tesis doctoral del primer autor, Dr. Laith Alzubaidi, siendo José Santamaría investigador invitado por el autor del artículo en la co-supervision de dicha tesis doctoral, correspondiendo este con uno de los varios artículos científicos que fueron desarrollados y publicados durante y después de la tesis doctoral del Dr. Alzubaidi.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s11042-021-10942-9
https://hdl.handle.net/10953/2325
url https://doi.org/10.1007/s11042-021-10942-9
https://hdl.handle.net/10953/2325
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Multimedia Tools and Applications 2021; 81:13289–13317
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
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repository.mail.fl_str_mv
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