Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen C. albicans. Our system entitled Candescence automatically detects C. albicans cells from Differential Image Contrast microscopy, and labels each detected cell with one of nine vegetative, mating-competent or filamentous morphologies. The software is based upon a fully convolutional one-stage object detector and exploits a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple vegetative forms to more complex filamentous architectures. Candescence achieves very good performance on this difficult learning set which has substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology, we develop models using generative adversarial networks and identify subcomponents of the latent space which control technical variables, developmental trajectories or morphological switches. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology.

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DEEP LEARNING APPROACH TO FACE CONDITIONING USING INVERTIBLE CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS (ICGAN)

International Journal of Innovative Research in Computer Science & Technology ◽

10.21276/ijircst.2020.8.3.24 ◽

2020 ◽

Vol 8 (3) ◽

Author(s):

Utpal Srivastav ◽

Vikas Thada ◽

Amit Kumar ◽

Maulik Garach ◽

Adit Paliwal

Keyword(s):

Deep Learning ◽

Generative Adversarial Networks ◽

Learning Approach ◽

Adversarial Networks

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Improving Cloud-based ECG Monitoring, Detection and Classification using GAN

10.54216/fpa.020201 ◽

2020 ◽

pp. 42-49

Author(s):

admin admin ◽

◽

Monika Gupta

Keyword(s):

Deep Learning ◽

Training Data ◽

Generative Adversarial Networks ◽

Learning Models ◽

Healthcare Applications ◽

Ecg Signals ◽

Cardiac Abnormalities ◽

Adversarial Networks ◽

Data Points

Internet of Things (IoT) based healthcare applications have grown exponentially over the past decade. With the increasing number of fatalities due to cardiovascular diseases (CVD), it is the need of the hour to detect any signs of cardiac abnormalities as early as possible. This calls for automation on the detection and classification of said cardiac abnormalities by physicians. The problem here is that, there is not enough data to train Deep Learning models to classify ECG signals accurately because of sensitive nature of data and the rarity of certain cases involved in CVDs. In this paper, we propose a framework which involves Generative Adversarial Networks (GAN) to create synthetic training data for the classes with less data points to improve the performance of Deep Learning models trained with the dataset. With data being input from sensors via cloud and this model to classify the ECG signals, we expect the framework to be functional, accurate and efficient.

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COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

Applied Sciences ◽

10.3390/app11167174 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7174

Author(s):

Amal A. Al-Shargabi ◽

Jowharah F. Alshobaili ◽

Abdulatif Alabdulatif ◽

Naseem Alrobah

Keyword(s):

Deep Learning ◽

Generative Adversarial Networks ◽

Learning Approach ◽

Medical Systems ◽

Perfect Agreement ◽

Adversarial Networks ◽

Lack Of Physicians ◽

Chest X Ray ◽

Novel Coronavirus ◽

Treat All

COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets.

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Deep Learning Approach To Face Conditioning Using Invertible Conditional Generative Adversarial Networks (ICGAN)

SSRN Electronic Journal ◽

10.2139/ssrn.3670849 ◽

2020 ◽

Author(s):

Mr. Utpal Shrivastava ◽

Dr. Vikas Thada ◽

Amit Kumar ◽

Maulik Garach ◽

Adit Paliwal

Keyword(s):

Deep Learning ◽

Generative Adversarial Networks ◽

Learning Approach ◽

Adversarial Networks

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Classification of Neurodegenerative Diseases Based on VGG 19 Deep Transfer Learning Architecture: A Deep Learning Approach

Bioscience Biotechnology Research Communications ◽

10.21786/bbrc/13.4/51 ◽

2020 ◽

Vol 13 (4) ◽

pp. 1972-1980

Author(s):

Kirti Raj Bhatele

Keyword(s):

Deep Learning ◽

Neurodegenerative Diseases ◽

Transfer Learning ◽

Learning Approach

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Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽

10.3390/s21154953 ◽

2021 ◽

Vol 21 (15) ◽

pp. 4953

Author(s):

Sara Al-Emadi ◽

Abdulla Al-Ali ◽

Abdulaziz Al-Ali

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Learning Algorithms ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Networks ◽

Detection And Identification ◽

Learning Techniques ◽

The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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