Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Lin, Cheng‐Jian; Jeng, Shiou-Yun

doi:10.3390/diagnostics10090662

Cited by 22 publications

(19 citation statements)

References 29 publications

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“… Sharma & Mehra (2020b) proposed a new transfer learning method for the AlexNet CNN and attained an accuracy of 89.31%. The authors ( Lin & Jeng, 2020 ) proposed a novel CNN that is optimized using the uniform experimental design method. Although the method was time-efficient, it achieved a low mean accuracy of 88.41%.…”

Section: Related Workmentioning

confidence: 99%

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.

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Section: Related Workmentioning

confidence: 99%

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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“…Rather than designing new models, the researcher [ 8 ] applied a Meta-heuristic optimization algorithm to boost the performance of CNN for medical image classification. Another study by [ 9 ] optimized the CNN model for histopathological image classification and reported the significant performance of the model. They have followed UED (uniform experimental design) and performed the parameter optimization of breast cancer histopathological images.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive momentum method for medical image classification using convolutional neural network

2022

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Background AI for medical diagnosis has made a tremendous impact by applying convolutional neural networks (CNNs) to medical image classification and momentum plays an essential role in stochastic gradient optimization algorithms for accelerating or improving training convolutional neural networks. In traditional optimizers in CNNs, the momentum is usually weighted by a constant. However, tuning hyperparameters for momentum can be computationally complex. In this paper, we propose a novel adaptive momentum for fast and stable convergence. Method Applying adaptive momentum rate proposes increasing or decreasing based on every epoch's error changes, and it eliminates the need for momentum hyperparameter optimization. We tested the proposed method with 3 different datasets: REMBRANDT Brain Cancer, NIH Chest X-ray, COVID-19 CT scan. We compared the performance of a novel adaptive momentum optimizer with Stochastic gradient descent (SGD) and other adaptive optimizers such as Adam and RMSprop. Results Proposed method improves SGD performance by reducing classification error from 6.12 to 5.44%, and it achieved the lowest error and highest accuracy compared with other optimizers. To strengthen the outcomes of this study, we investigated the performance comparison for the state-of-the-art CNN architectures with adaptive momentum. The results shows that the proposed method achieved the highest with 95% compared to state-of-the-art CNN architectures while using the same dataset. The proposed method improves convergence performance by reducing classification error and achieves high accuracy compared with other optimizers.

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“…Zhou et al (18) used UED to design Xiaokeyinshui extract combinations with different formulae for treating diabetes mellitus in mice. Lin and Jeng (19) also used UED to optimize the convolution kernel, channel number, and other parameters of deep CNNs to classify breast cancer tissue images. Similarly, UED was used in this study to optimize the parameters of the constructed 3D-CNN to improve the robustness and accuracy of the network.…”

Section: Introductionmentioning

confidence: 99%

Using Three-dimensional Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Lin¹,

Lin²

2021

Sensors and Materials

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Alzheimer's disease (AD) is an irreversible neurodegenerative disease. Pathology shows atrophy of brain tissue, senile plaques, neurofibrillary tangles, and so forth. Magnetic resonance imaging (MRI) is the most sensitive brain imaging method in a clinic, which provides detailed anatomical structure information of the brain and is commonly studied with pattern recognition methods for AD diagnosis. Most existing methods extract hand-crafted imaging features or brain region-of-interest images to train a classifier to recognize AD. In this study, a threedimensional convolutional neural network (3D-CNN) is implemented to detect AD. The proposed network is trained with 3D magnetic resonance (MR) images to extract the spatial features. A uniform experimental design is used to optimize the network parameters and improve the 3D-CNN performance. To ensure satisfactory performance on a small amount of training data, a transfer learning technology is proposed to improve the recognition rate of the 3D-CNN. In addition, the uniform experimental design (UED) method is used to determine the optimal parameter combination of the network and improve the 3D-CNN performance. The validation data in this study are from the Open Access Series of Imaging Studies (OASIS), where the OASIS-1 data set is used as the original data set and the 3D-CNN is trained as the pretraining model. Experimental results show that when 10, 30, 60, and 90% of the OASIS-2 data set are used to train the pre-trained 3D-CNN, the average accuracy reaches 74.66, 86.99, 94.58, and 97.02%, respectively. In addition, compared with the original manual design parameters, the proposed 3D-CNN with the best parameter combination improves the recognition rate by 2.07 percentage points.

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Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Cited by 22 publications

References 29 publications

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

A novel adaptive momentum method for medical image classification using convolutional neural network

Using Three-dimensional Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

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