Complementary Performances of Convolutional and Capsule Neural Networks on Classifying Microfluidic Images of Dividing Yeast Cells

AbstractMicrofluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapsed images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance at detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidics-based assays of yeast replicative aging.

Download Full-text

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells

PLoS ONE ◽

10.1371/journal.pone.0246988 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0246988

Author(s):

Mehran Ghafari ◽

Justin Clark ◽

Hao-Bo Guo ◽

Ruofan Yu ◽

Yu Sun ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Data Augmentation ◽

Time Lapse ◽

Yeast Cells ◽

Training Dataset ◽

Robust Performance ◽

Replicative Aging ◽

Specific Category ◽

Learning Architectures

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.

Download Full-text

Robust deep learning pipeline for PVC beats localization

Technology and Health Care ◽

10.3233/thc-218045 ◽

2021 ◽

Vol 29 ◽

pp. 475-486

Author(s):

Bohdan Petryshak ◽

Illia Kachko ◽

Mykola Maksymenko ◽

Oles Dobosevych

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Premature Ventricular Contraction ◽

Ecg Signal ◽

Ventricular Contraction ◽

Localization Task ◽

Robust Performance ◽

Extensive Evaluation ◽

Decoder Architecture ◽

Public Datasets

BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

Download Full-text

Classification & Detection of Vehicles using Deep Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f3905.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 181-189

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Transportation Systems ◽

Coarse Grained ◽

Vehicle Classification ◽

Training Dataset ◽

License Plate ◽

Original Dataset ◽

Detection Of Objects

The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. However, theexisting methods of vehicle classification and detection are highly complex which provides coarse-grained outcomesbecause of underfitting or overfitting of the model. Due toadvanced accomplishmentsof the Deep Learning, it was efficiently implemented to image classification and detection of objects. This proposed paper come up with a new approach which makes use of convolutional neural networks concept in Deep Learning.It consists of two steps: i) vehicle classification ii) vehicle license plate recognition. Numerous classicmodules of neural networks hadbeen implemented in training and testing the vehicle classification and detection of license plate model, such as CNN (convolutional neural networks), TensorFlow, and Tesseract-OCR. The suggestedtechnique candetermine the vehicle type, number plate and other alternative dataeffectively. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original dataset (training) and enriched dataset (testing), this customized model(algorithm) can achieve best outcomewith a standard accuracy of around 97.32% inclassification and detection of vehicles. By enlarging the quantity of the training dataset, the loss function and mislearning rate declines progressively. Therefore, this proposedmodelwhich uses DeepLearning hadbetterperformance and flexibility. When compared to outstandingtechniques in the strategicImage datasets, this deep learning modelscan gethigher competitor outcomes. Eventually, the proposed system suggests modern methods for advancementof the customized model and forecasts the progressivegrowth of deep learningperformance in the explorationof artificial intelligence (AI) &machine learning (ML) techniques.

Download Full-text

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities

Bioinformatics ◽

10.1093/bioinformatics/btz339 ◽

2019 ◽

Vol 35 (14) ◽

pp. i269-i277 ◽

Cited By ~ 24

Author(s):

Ameni Trabelsi ◽

Mohamed Chaabane ◽

Asa Ben-Hur

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Rna Binding ◽

Binding Specificity ◽

Training Data ◽

Supplementary Information ◽

Recurrent Networks ◽

Dna And Rna ◽

Learning Architectures ◽

Dna And Rna Binding

Abstract Motivation Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurrent neural networks (RNNs) and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies the relative merit of the various architectures remains unclear. Results In this study we present a systematic exploration of deep learning architectures for predicting DNA- and RNA-binding specificity. For this purpose, we present deepRAM, an end-to-end deep learning tool that provides an implementation of a wide selection of architectures; its fully automatic model selection procedure allows us to perform a fair and unbiased comparison of deep learning architectures. We find that deeper more complex architectures provide a clear advantage with sufficient training data, and that hybrid CNN/RNN architectures outperform other methods in terms of accuracy. Our work provides guidelines that can assist the practitioner in choosing an appropriate network architecture, and provides insight on the difference between the models learned by convolutional and recurrent networks. In particular, we find that although recurrent networks improve model accuracy, this comes at the expense of a loss in the interpretability of the features learned by the model. Availability and implementation The source code for deepRAM is available at https://github.com/MedChaabane/deepRAM. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Exemplar models are useful and deep neural networks overcome their limitations: A commentary on Ambridge (2020)

First Language ◽

10.1177/0142723720905765 ◽

2020 ◽

Vol 40 (5-6) ◽

pp. 612-615

Author(s):

James L. McClelland

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Language Processing ◽

Deep Neural Networks ◽

Machine Intelligence ◽

Human Language ◽

Language Usage ◽

Primitive Elements ◽

Exemplar Models ◽

Learning Architectures

Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up each example, and such architectures have not been as successful as deep neural networks in capturing language usage and meaning. More work is needed bringing contemporary deep learning architectures used in machine intelligence to the effort to understand human language processing.

Download Full-text

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

PLoS ONE ◽

10.1371/journal.pone.0242013 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242013

Author(s):

Hongyu Wang ◽

Hong Gu ◽

Pan Qin ◽

Jia Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Characteristic Curve ◽

Healthcare Delivery ◽

Chest Radiographs ◽

Training Dataset ◽

Deep Convolutional Neural Networks ◽

Support Tool

Background Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs. Methods and findings We developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance. Conclusions In this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.

Download Full-text

Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

10.54216/fpa.030103 ◽

2021 ◽

pp. 43-53

Author(s):

admin admin ◽

◽

Adnan Mohsin Abdulazeez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Classification Accuracy ◽

Machine Learning Techniques ◽

Deep Convolutional Neural Networks ◽

Learning Techniques ◽

Good Classification ◽

Learning Architectures ◽

Ecg Data

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

Download Full-text

How Convolutional Neural Networks Diagnose Plant Disease

Plant Phenomics ◽

10.1155/2019/9237136 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 7

Author(s):

Yosuke Toda ◽

Fumio Okura

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Plant Disease ◽

Disease Diagnosis ◽

Black Box ◽

Plant Diseases ◽

Training Dataset ◽

Great Success ◽

Human Decision

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

Download Full-text

An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning

Applied Sciences ◽

10.3390/app11188441 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8441

Author(s):

Anh-Cang Phan ◽

Ngoc-Hoang-Quyen Nguyen ◽

Thanh-Ngoan Trieu ◽

Thuong-Cang Phan

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Training Dataset ◽

Economic Losses ◽

Drowsy Driving ◽

Training Time ◽

Road Users ◽

Fast Training ◽

Learning Techniques

Drowsy driving is one of the common causes of road accidents resulting in injuries, even death, and significant economic losses to drivers, road users, families, and society. There have been many studies carried out in an attempt to detect drowsiness for alert systems. However, a majority of the studies focused on determining eyelid and mouth movements, which have revealed many limitations for drowsiness detection. Besides, physiological measures-based studies may not be feasible in practice because the measuring devices are often not available on vehicles and often uncomfortable for drivers. In this research, we therefore propose two efficient methods with three scenarios for doze alert systems. The former applies facial landmarks to detect blinks and yawns based on appropriate thresholds for each driver. The latter uses deep learning techniques with two adaptive deep neural networks based on MobileNet-V2 and ResNet-50V2. The second method analyzes the videos and detects driver’s activities in every frame to learn all features automatically. We leverage the advantage of the transfer learning technique to train the proposed networks on our training dataset. This solves the problem of limited training datasets, provides fast training time, and keeps the advantage of the deep neural networks. Experiments were conducted to test the effectiveness of our methods compared with other methods. Empirical results demonstrate that the proposed method using deep learning techniques can achieve a high accuracy of 97% . This study provides meaningful solutions in practice to prevent unfortunate automobile accidents caused by drowsiness.

Download Full-text

Fusion-Based Semantic Segmentation Using Deep Learning Architecture in Case of Very Small Training Dataset

International Journal of Image and Graphics ◽

10.1142/s0219467822500437 ◽

2021 ◽

Author(s):

Ganesh R. Padalkar ◽

Madhuri B. Khambete

Keyword(s):

Deep Learning ◽

Real Life ◽

Class Imbalance ◽

Semantic Segmentation ◽

Training Dataset ◽

Processing Step ◽

Effective Training ◽

Unsupervised Algorithms ◽

Learning Architectures ◽

Small Dataset

Semantic segmentation is a pre-processing step in computer vision-based applications. It is the task of assigning a predefined class label to every pixel of an image. Several supervised and unsupervised algorithms are available to classify pixels of an image into predefined object classes. The algorithms, such as random forest and SVM are used to obtain the semantic segmentation. Recently, convolutional neural network (CNN)-based architectures have become popular for the tasks of object detection, object recognition, and segmentation. These deep architectures perform semantic segmentation with far better accuracy than the algorithms that were used earlier. CNN-based deep learning architectures require a large dataset for training. In real life, some of the applications may not have sufficient good quality samples for training of deep learning architectures e.g. medical applications. Such a requirement initiated a need to have a technique of effective training of deep learning architecture in case of a very small dataset. Class imbalance is another challenge in the process of training deep learning architecture. Due to class imbalance, the classifier overclassifies classes with large samples. In this paper, the challenge of training a deep learning architecture with a small dataset and class imbalance is addressed by novel fusion-based semantic segmentation technique which improves segmentation of minor and major classes.

Download Full-text