Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation. The field...

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Autores principales:	Anil Johny, K. N. Madhusoodanan
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/8ba3530317e94c57829fe8ebcc2ce25b
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id	oai:doaj.org-article:8ba3530317e94c57829fe8ebcc2ce25b
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spelling	oai:doaj.org-article:8ba3530317e94c57829fe8ebcc2ce25b2021-11-08T02:36:15ZDynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images1748-671810.1155/2021/5557168https://doaj.org/article/8ba3530317e94c57829fe8ebcc2ce25b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5557168https://doaj.org/toc/1748-6718Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation. The field of pathology has advanced so rapidly that it is possible to obtain high-quality images from glass slides. Patches from the region of interest in histopathology images are extracted and trained using artificial neural network models. The trained model primarily analyzes and predicts the histology images for the benign or malignant class to which it belongs. Classification of medical images focuses on the training of models with layers of abstraction to distinguish between these two classes with less false-positive rates. The learning rate is the crucial hyperparameter used during the training of deep convolutional neural networks (DCNN) to improve model accuracy. This work emphasizes the relevance of the dynamic learning rate than the fixed learning rate during the training of networks. The dynamic learning rate varies with preset conditions between the lower and upper boundaries and repeats at different iterations. The performance of the model thus improves and attains comparatively high accuracy with fewer iterations.Anil JohnyK. N. MadhusoodananHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Anil Johny K. N. Madhusoodanan Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images
description	Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation. The field of pathology has advanced so rapidly that it is possible to obtain high-quality images from glass slides. Patches from the region of interest in histopathology images are extracted and trained using artificial neural network models. The trained model primarily analyzes and predicts the histology images for the benign or malignant class to which it belongs. Classification of medical images focuses on the training of models with layers of abstraction to distinguish between these two classes with less false-positive rates. The learning rate is the crucial hyperparameter used during the training of deep convolutional neural networks (DCNN) to improve model accuracy. This work emphasizes the relevance of the dynamic learning rate than the fixed learning rate during the training of networks. The dynamic learning rate varies with preset conditions between the lower and upper boundaries and repeats at different iterations. The performance of the model thus improves and attains comparatively high accuracy with fewer iterations.
format	article
author	Anil Johny K. N. Madhusoodanan
author_facet	Anil Johny K. N. Madhusoodanan
author_sort	Anil Johny
title	Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images
title_short	Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images
title_full	Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images
title_fullStr	Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images
title_full_unstemmed	Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images
title_sort	dynamic learning rate in deep cnn model for metastasis detection and classification of histopathology images
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8ba3530317e94c57829fe8ebcc2ce25b
work_keys_str_mv	AT aniljohny dynamiclearningrateindeepcnnmodelformetastasisdetectionandclassificationofhistopathologyimages AT knmadhusoodanan dynamiclearningrateindeepcnnmodelformetastasisdetectionandclassificationofhistopathologyimages
_version_	1718443115160797184

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

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