Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

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Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽

10.3390/computers10060082 ◽

2021 ◽

Vol 10 (6) ◽

pp. 82

Author(s):

Ahmad O. Aseeri

Keyword(s):

Deep Learning ◽

Cardiac Arrhythmias ◽

Large Scale ◽

Clinical Decision Making ◽

Probabilistic Approach ◽

Classification Model ◽

Gating Mechanism ◽

Uncertainty Estimates ◽

Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

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Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning

Applied Sciences ◽

10.3390/app11093952 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3952

Author(s):

Shimin Tang ◽

Zhiqiang Chen

Keyword(s):

Deep Learning ◽

Natural Disaster ◽

Scene Understanding ◽

Computing Methods ◽

Classification Model ◽

Learning Approach ◽

Learning Models ◽

Damage Level ◽

Feature Extractor ◽

Mobile Imaging

With the ubiquitous use of mobile imaging devices, the collection of perishable disaster-scene data has become unprecedentedly easy. However, computing methods are unable to understand these images with significant complexity and uncertainties. In this paper, the authors investigate the problem of disaster-scene understanding through a deep-learning approach. Two attributes of images are concerned, including hazard types and damage levels. Three deep-learning models are trained, and their performance is assessed. Specifically, the best model for hazard-type prediction has an overall accuracy (OA) of 90.1%, and the best damage-level classification model has an explainable OA of 62.6%, upon which both models adopt the Faster R-CNN architecture with a ResNet50 network as a feature extractor. It is concluded that hazard types are more identifiable than damage levels in disaster-scene images. Insights are revealed, including that damage-level recognition suffers more from inter- and intra-class variations, and the treatment of hazard-agnostic damage leveling further contributes to the underlying uncertainties.

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Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽

10.3390/s21093279 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3279

Author(s):

Maria Habib ◽

Mohammad Faris ◽

Raneem Qaddoura ◽

Manal Alomari ◽

Alaa Alomari ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Quality Assessment ◽

Transcript Level ◽

Assessment Process ◽

Classification Model ◽

Systematic Evaluation ◽

Transcript Levels ◽

Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

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KArSL

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3423420 ◽

2021 ◽

Vol 20 (1) ◽

pp. 1-19

Author(s):

Ala Addin I. Sidig ◽

Hamzah Luqman ◽

Sabri Mahmoud ◽

Mohamed Mohandes

Keyword(s):

Deep Learning ◽

Sign Language ◽

Markov Models ◽

Body Language ◽

Arab Countries ◽

Microsoft Kinect ◽

Classification Model ◽

Language Recognition ◽

Sign Language Recognition ◽

Arabic Sign Language

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.

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A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

Neural Processing Letters ◽

10.1007/s11063-021-10555-1 ◽

2021 ◽

Author(s):

Koyel Datta Gupta ◽

Deepak Kumar Sharma ◽

Shakib Ahmed ◽

Harsh Gupta ◽

Deepak Gupta ◽

...

Keyword(s):

Deep Learning ◽

Image Classification ◽

Classification Model ◽

Histopathological Image ◽

Histopathological Image Classification

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Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

Applied Sciences ◽

10.3390/app11020760 ◽

2021 ◽

Vol 11 (2) ◽

pp. 760

Author(s):

Yun-ji Kim ◽

Hyun Chin Cho ◽

Hyun-chong Cho

Keyword(s):

Deep Learning ◽

Disease Diagnosis ◽

Computer Aided Diagnosis ◽

Medical Data ◽

Classification Model ◽

Training Dataset ◽

Generative Adversarial Networks ◽

Original Training ◽

Computer Aided ◽

Aided Diagnosis

Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

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Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽

10.3390/ani11020357 ◽

2021 ◽

Vol 11 (2) ◽

pp. 357

Author(s):

Dae-Hyun Jung ◽

Na Yeon Kim ◽

Sang Ho Moon ◽

Changho Jhin ◽

Hak-Jin Kim ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

Model Development ◽

Classification Model ◽

Intelligence Analysis ◽

Noise Filtering ◽

Meat Industry ◽

Mel Frequency Cepstral Coefficients ◽

Web Based ◽

The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

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Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽

10.3390/s21020555 ◽

2021 ◽

Vol 21 (2) ◽

pp. 555

Author(s):

Jui-Sheng Chou ◽

Chia-Hsuan Liu

Keyword(s):

Deep Learning ◽

Real Time ◽

Character Recognition ◽

Initial Period ◽

Control Point ◽

Recognition Rate ◽

Classification Model ◽

License Plate ◽

Construction Site ◽

Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

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