A new imaged-based quantitative reader for the gold immunochromatographic assay

Zeng, Nianyin; You, Yi; Xie, Lusheng; Zhang, Hong; Ye, Lishan; Hong, Wenxing; Li, Yurong

doi:10.1016/j.ijleo.2017.09.109

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…The bioinspired-algorithm may help retraining our AlexNet model. Particle swarm optimization (PSO) (35–37) and other methods will be tested. Cloud computing (38) in particular can be integrated into our method, to help diagnosis of remote patients.…”

Section: Resultsmentioning

confidence: 99%

Alcoholism Identification Based on an AlexNet Transfer Learning Model

et al. 2019

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Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10 −4 , and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set. Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images.

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Section: Resultsmentioning

confidence: 99%

Alcoholism Identification Based on an AlexNet Transfer Learning Model

et al. 2019

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“…In the future, we shall try particle swarm optimization (PSO) (Zeng et al, 2016c , d ) and other methods. The hardware of our model can be optimized using specific optimization method (Zeng et al, 2018 ).…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

et al. 2018

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Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%.Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.

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“…In the future, we will try to collect more samples and test more different structures for CMB detection. Meanwhile, the training cost long term is very high (Zeng et al, 2018), therefore it is necessary to optimize the algorithm to make the training fast (Zeng et al, 2016b,d). We will consider other precomputation and some optimization methods (Zhang et al, 2018a).…”

Section: Discussionmentioning

confidence: 99%

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

et al. 2019

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Cerebral micro-bleedings (CMBs) are small chronic brain hemorrhages that have many side effects. For example, CMBs can result in long-term disability, neurologic dysfunction, cognitive impairment and side effects from other medications and treatment. Therefore, it is important and essential to detect CMBs timely and in an early stage for prompt treatment. In this research, because of the limited labeled samples, it is hard to train a classifier to achieve high accuracy. Therefore, we proposed employing Densely connected neural network (DenseNet) as the basic algorithm for transfer learning to detect CMBs. To generate the subsamples for training and test, we used a sliding window to cover the whole original images from left to right and from top to bottom. Based on the central pixel of the subsamples, we could decide the target value. Considering the data imbalance, the cost matrix was also employed. Then, based on the new model, we tested the classification accuracy, and it achieved 97.71%, which provided better performance than the state of art methods.

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A new imaged-based quantitative reader for the gold immunochromatographic assay

Cited by 13 publications

References 15 publications

Alcoholism Identification Based on an AlexNet Transfer Learning Model

Alcoholism Identification Based on an AlexNet Transfer Learning Model

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

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