Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Zhang, Yudong; Satapathy, Suresh Chandra; Wu, Di; Guttery, David S.; Górriz, Juan Manuel; Wang, Shuihua

doi:10.1007/s40747-020-00218-4

Cited by 38 publications

(19 citation statements)

References 22 publications

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“…We tested other values of , and found achieved the best performance on the test set. ELU has already shown its superiority to other NLAFs in ductal carcinoma in situ (Zhang 2021 ), optical gating trace retrieval (Xu et al 2021 ), etc. Figure 4 c–e shows the curves of the other three NLAFs.…”

Section: Proposed Elucnnmentioning

confidence: 99%

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client–server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of $$94.41\pm 0.98$$ 94.41 ± 0.98 , a specificity of $$94.84\pm 1.21$$ 94.84 ± 1.21 , an accuracy of $$94.62\pm 0.96$$ 94.62 ± 0.96 , and an F1 score of $$94.61\pm 0.95$$ 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

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Section: Proposed Elucnnmentioning

confidence: 99%

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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“…RAP can alleviate the problem of information loss from max pooling operation and discriminative information loss from average pooling operation. Rank-based weighted pooling [ 30 ] (RWP) operation assigns weight p r according to the size of the value. The value as the response value is obtained by multiplying the feature map matrix and the weight matrix.…”

Section: Variable Of Pooling Kernelmentioning

confidence: 99%

Pooling Operations in Deep Learning: From “Invariable” to “Variable”

Tao

XiaoYu

et al. 2022

BioMed Research International

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Deep learning has become a research hotspot in multimedia, especially in the field of image processing. Pooling operation is an important operation in deep learning. Pooling operation can reduce the feature dimension, the number of parameters, the complexity of computation, and the complexity of time. With the development of deep learning models, pooling operation has made great progress. The main contributions of this paper on pooling operation are as follows: firstly, the steps of the pooling operation are summarized as the pooling domain, pooling kernel, step size, activation value, and response value. Secondly, the expression form of pooling operation is standardized. From the perspective of “invariable” to “variable,” this paper analyzes the pooling domain and pooling kernel in the pooling operation. Pooling operation can be classified into four categories: invariable of pooling domain, variable of pooling domain, variable of pooling kernel, and the pooling of invariable “+” variable. Finally, the four types of pooling operation are summarized and discussed with their advantages and disadvantages. There is great significance to the research of pooling operations and the iterative updating of deep learning models.

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“…In this order, convolution is performed from left to right. This is the principle of convolution layer [30]. The convolution layer formula is as follows:…”

Section: Convolution Layermentioning

confidence: 99%

Alcoholic brain injury via 8-layer customized deep convolution neural network

Zhu

Brown

2021

STEMedicine

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Alcohol can act quickly in the human body and alter mood and behavior. If alcohol is consumed in excess, it will accumulate in the organs of the body, especially in the liver and brain. To a certain extent, the symptoms of alcoholism will appear. So far, the main method of diagnosis of alcoholic brain injury is through MRI images by radiologists. However, this is a very subjective diagnosis. Radiologists may be affected by external factors, such as physical discomfort, lack of rest, inattention, etc., resulting in diagnostic errors. In this paper, we proposed a novel 8-layer customized deep convolution neural network for alcoholic brain injury detection, which contains five convolution layers, five pooling layers, and three fully connected layers. There are three improvements in this paper, (i) Based on deep learning, we proposed a method for automatic diagnosis of alcoholic brain injury; (ii) We introduced Dropout to the proposed structure to improve robustness; (iii) Compared with other state-of-the-art approaches, the proposed structure is more efficient. The experimental results showed that the sensitivity, specificity, precision, accuracy, F1, MCC and FMI were 96.14±1.99, 96.20±1.47, 95.98±1.54, 96.17±1.55, 96.05±1.62, 93.34±3.11, 96.06±1.62 respectively. According to comparison results, our method performed the best. The proposed model is effective in detecting alcoholic brain injury based on MRI images.

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Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Cited by 38 publications

References 22 publications

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

Pooling Operations in Deep Learning: From “Invariable” to “Variable”

Alcoholic brain injury via 8-layer customized deep convolution neural network

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