scholarly journals Single-Cell Conventional Pap Smear Image Classification Using Pre-Trained Deep Neural Network Architectures

2021 ◽  
Author(s):  
Mohammed Aliy Mohammed ◽  
Fetulhak Abdurahman ◽  
Yodit Abebe Ayalew
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Image Classification ◽  
Deep Neural Networks ◽  
Pap Smear ◽  
Experimental Result ◽  
Network Architectures ◽  
Skilled Manpower ◽  
Average Accuracy ◽  
Neural Network Architectures

Abstract Background: Automating Pap smear based cervical cancer screening could alleviate the challenge related to the shortage of skilled manpower, mainly pathologists in developing countries. The astonishing accuracy and reproducibility of deep neural networks in computer vision have made them a frontline candidate to tackle such challenges. In this regard, the main purpose of this research project is to classify single-cell Pap smear microscopic images using the architectures of the pre-trained deep convolutional neural network image classifiers. We have trained five class single-cell Pap smear images from SIPaKMeD on top ten pre-trained image classification architectures. The architectures were selected from Keras Applications based on their top 1% accuracy. Then, the output layers of the selected architectures were fine-tuned from 1000 classes to five classes and retrained. Results: Our experimental result demonstrated that DenseNet169 outperformed the selected 10 pre-trained architectures with an average accuracy, precision, recall and f1-scores of 0.990, 0.974, 0.974 and 0.974, respectively. Moreover, it dashed the benchmark accuracy proposed by the creators of the dataset with 3.70%. Conclusions: Even though the size of DenseNet169 is small compared to the experimented architectures, still it is not suitable for mobile and edged devices. It is required to experiment with mobile or small size image classification neural network architectures.

scholarly journals Single-cell conventional pap smear image classification using pre-trained deep neural network architectures

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammed Aliy Mohammed ◽  
Fetulhak Abdurahman ◽  
Yodit Abebe Ayalew
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Computer Vision ◽  
Single Cell ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Pap Smear ◽  
Experimental Result ◽  
Network Architectures ◽  
Average Accuracy

Abstract Background Automating cytology-based cervical cancer screening could alleviate the shortage of skilled pathologists in developing countries. Up until now, computer vision experts have attempted numerous semi and fully automated approaches to address the need. Yet, these days, leveraging the astonishing accuracy and reproducibility of deep neural networks has become common among computer vision experts. In this regard, the purpose of this study is to classify single-cell Pap smear (cytology) images using pre-trained deep convolutional neural network (DCNN) image classifiers. We have fine-tuned the top ten pre-trained DCNN image classifiers and evaluated them using five class single-cell Pap smear images from SIPaKMeD dataset. The pre-trained DCNN image classifiers were selected from Keras Applications based on their top 1% accuracy. Results Our experimental result demonstrated that from the selected top-ten pre-trained DCNN image classifiers DenseNet169 outperformed with an average accuracy, precision, recall, and F1-score of 0.990, 0.974, 0.974, and 0.974, respectively. Moreover, it dashed the benchmark accuracy proposed by the creators of the dataset with 3.70%. Conclusions Even though the size of DenseNet169 is small compared to the experimented pre-trained DCNN image classifiers, yet, it is not suitable for mobile or edge devices. Further experimentation with mobile or small-size DCNN image classifiers is required to extend the applicability of the models in real-world demands. In addition, since all experiments used the SIPaKMeD dataset, additional experiments will be needed using new datasets to enhance the generalizability of the models.

scholarly journals Neural networks for classification of strokes in electrical impedance tomography on a 3D head model

2022 ◽  
Vol 4 (4) ◽  
pp. 1-22
Author(s):  
Valentina Candiani ◽  
◽  
Matteo Santacesaria ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Network Architectures ◽  
Impedance Tomography ◽  
Average Accuracy ◽  
Neural Network Architectures ◽  
Fully Connected

<abstract><p>We consider the problem of the detection of brain hemorrhages from three-dimensional (3D) electrical impedance tomography (EIT) measurements. This is a condition requiring urgent treatment for which EIT might provide a portable and quick diagnosis. We employ two neural network architectures - a fully connected and a convolutional one - for the classification of hemorrhagic and ischemic strokes. The networks are trained on a dataset with $ 40\, 000 $ samples of synthetic electrode measurements generated with the complete electrode model on realistic heads with a 3-layer structure. We consider changes in head anatomy and layers, electrode position, measurement noise and conductivity values. We then test the networks on several datasets of unseen EIT data, with more complex stroke modeling (different shapes and volumes), higher levels of noise and different amounts of electrode misplacement. On most test datasets we achieve $ \geq 90\% $ average accuracy with fully connected neural networks, while the convolutional ones display an average accuracy $ \geq 80\% $. Despite the use of simple neural network architectures, the results obtained are very promising and motivate the applications of EIT-based classification methods on real phantoms and ultimately on human patients.</p></abstract>

scholarly journals Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition

2020 ◽  
Author(s):  
Ronnypetson Da Silva ◽  
Valter M. Filho ◽  
Mario Souza
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks ◽  
Speech Emotion Recognition ◽  
Network Architectures ◽  
Shared Representations ◽  
Multiple Datasets ◽  
Neural Network Architectures

Many works that apply Deep Neural Networks (DNNs) to Speech Emotion Recognition (SER) use single datasets or train and evaluate the models separately when using multiple datasets. Those datasets are constructed with specific guidelines and the subjective nature of the labels for SER makes it difficult to obtain robust and general models. We investigate how DNNs learn shared representations for different datasets in both multi-task and unified setups. We also analyse how each dataset benefits from others in different combinations of datasets and popular neural network architectures. We show that the longstanding belief of more data resulting in more general models doesn’t always hold for SER, as different dataset and meta-parameter combinations hold the best result for each of the analysed datasets.

scholarly journals HELLO: improved neural network architectures and methodologies for small variant calling

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anand Ramachandran ◽  
Steven S. Lumetta ◽  
Eric W. Klee ◽  
Deming Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Recognition ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Method Development ◽  
Variant Calling ◽  
Network Architectures ◽  
Sequencing Data ◽  
Neural Network Architectures

Abstract Background Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy. In this paper, we explore an alternative approach to designing Deep Neural Networks for variant calling, where we use meticulously designed Deep Neural Network architectures and customized variant inference functions that account for the underlying nature of sequencing data instead of converting the problem to one of image recognition. Results Results from 27 whole-genome variant calling experiments spanning Illumina, PacBio and hybrid Illumina-PacBio settings suggest that our method allows vastly smaller Deep Neural Networks to outperform the Inception-v3 architecture used in DeepVariant for indel and substitution-type variant calls. For example, our method reduces the number of indel call errors by up to 18%, 55% and 65% for Illumina, PacBio and hybrid Illumina-PacBio variant calling respectively, compared to a similarly trained DeepVariant pipeline. In these cases, our models are between 7 and 14 times smaller. Conclusions We believe that the improved accuracy and problem-specific customization of our models will enable more accurate pipelines and further method development in the field. HELLO is available at https://github.com/anands-repo/hello

scholarly journals Using Summary Layers to Probe Neural Network Behaviour

2020 ◽  
Vol 32 (2) ◽  
Author(s):  
Marelie Hattingh Davel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Network Architectures ◽  
Feedforward Networks ◽  
Empirical Results ◽  
Analysis Process ◽  
Generalisation Ability ◽  
Neural Network Architectures ◽  
Fully Connected

No framework exists that can explain and predict the generalisation ability of deep neural networks in general circumstances. In fact, this question has not been answered for some of the least complicated of neural network architectures: fully-connected feedforward networks with rectified linear activations and a limited number of hidden layers. For such an architecture, we show how adding a summary layer to the network makes it more amenable to analysis, and allows us to define the conditions that are required to guarantee that a set of samples will all be classified correctly. This process does not describe the generalisation behaviour of these networks, but produces a number of metrics that are useful for probing their learning and generalisation behaviour. We support the analytical conclusions with empirical results, both to confirm that the mathematical guarantees hold in practice, and to demonstrate the use of the analysis process.

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