Deep learning systems for automatic diagnosis of infant cry signals

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine

Oral Radiology ◽

10.1007/s11282-021-00538-2 ◽

2021 ◽

Author(s):

Mizuho Mori ◽

Yoshiko Ariji ◽

Motoki Fukuda ◽

Tomoya Kitano ◽

Takuma Funakoshi ◽

...

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

Classification Performance ◽

Learning Systems ◽

Learning Technology ◽

System 2 ◽

Potential Benefits ◽

System 1 ◽

Periapical Radiography

Abstract Objectives The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques. Methods We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively. The learning model of system 1 was created with only the classification process, whereas system 2 consisted of both the segmentation and classification models. In each model, 500 epochs of training were performed using AlexNet and U-net for classification and segmentation, respectively. The segmentation results were evaluated by the intersection over union method, with values of 0.6 or more considered as success. The classification results were compared between the two systems. Results The segmentation performance of system 2 was recall, precision, and F measure of 0.937, 0.961, and 0.949, respectively. System 2 showed better classification performance values than those obtained by system 1. The area under the receiver operating characteristic curve values differed significantly between system 1 (0.649) and system 2 (0.927). Conclusions The deep learning systems we created appeared to have potential benefits in evaluation of the technical positioning quality of periapical radiographs through the use of segmentation and classification functions.

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Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Frontiers in Public Health ◽

10.3389/fpubh.2021.670352 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ashwini K ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Chuan-Yu Chang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Infant Cry ◽

Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures

Sensors ◽

10.3390/s22020506 ◽

2022 ◽

Vol 22 (2) ◽

pp. 506

Author(s):

Yu-Jin Seol ◽

Young-Jae Kim ◽

Yoon-Sang Kim ◽

Young-Woo Cheon ◽

Kwang-Gi Kim

Keyword(s):

Deep Learning ◽

Binary Classification ◽

Characteristic Curve ◽

Nasal Bone ◽

Facial Trauma ◽

Future Research ◽

Automatic Diagnosis ◽

3 Dimensional ◽

Computed Tomography Images ◽

Nasal Fractures

This paper reported a study on the 3-dimensional deep-learning-based automatic diagnosis of nasal fractures. (1) Background: The nasal bone is the most protuberant feature of the face; therefore, it is highly vulnerable to facial trauma and its fractures are known as the most common facial fractures worldwide. In addition, its adhesion causes rapid deformation, so a clear diagnosis is needed early after fracture onset. (2) Methods: The collected computed tomography images were reconstructed to isotropic voxel data including the whole region of the nasal bone, which are represented in a fixed cubic volume. The configured 3-dimensional input data were then automatically classified by the deep learning of residual neural networks (3D-ResNet34 and ResNet50) with the spatial context information using a single network, whose performance was evaluated by 5-fold cross-validation. (3) Results: The classification of nasal fractures with simple 3D-ResNet34 and ResNet50 networks achieved areas under the receiver operating characteristic curve of 94.5% and 93.4% for binary classification, respectively, both indicating unprecedented high performance in the task. (4) Conclusions: In this paper, it is presented the possibility of automatic nasal bone fracture diagnosis using a 3-dimensional Resnet-based single classification network and it will improve the diagnostic environment with future research.

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Automatic Diagnosis of Covid-19 Using Chest X-ray Images Through Deep Learning Models

10.1007/978-981-16-5078-9_26 ◽

2021 ◽

pp. 303-312

Author(s):

Siddharth Gupta ◽

Palak Aggarwal ◽

Sumeshwar Singh ◽

Shiv Ashish Dhondiyal ◽

Manisha Aeri ◽

...

Keyword(s):

Deep Learning ◽

Learning Models ◽

Automatic Diagnosis ◽

X Ray ◽

Chest X Ray

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Google and DeepMind: Deep Learning Systems in Ophthalmology

10.1007/978-3-030-78601-4_12 ◽

2021 ◽

pp. 161-176

Author(s):

Xinle Liu ◽

Akinori Mitani ◽

Terry Spitz ◽

Derek J. Wu ◽

Joseph R. Ledsam

Keyword(s):

Deep Learning ◽

Learning Systems

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Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems

2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC) ◽

10.1109/hipc.2019.00036 ◽

2019 ◽

Cited By ~ 1

Author(s):

Swagath Venkataramani ◽

Vijayalakshmi Srinivasan ◽

Jungwook Choi ◽

Philip Heidelberger ◽

Leland Chang ◽

...

Keyword(s):

Deep Learning ◽

Learning Systems

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Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems

American Journal of Ophthalmology ◽

10.1016/j.ajo.2020.03.035 ◽

2020 ◽

Vol 216 ◽

pp. 140-146

Author(s):

Hee Kyung Yang ◽

Young Jae Kim ◽

Jae Yun Sung ◽

Dong Hyun Kim ◽

Kwang Gi Kim ◽

...

Keyword(s):

Deep Learning ◽

Optic Neuropathy ◽

Learning Systems ◽

Glaucomatous Optic Neuropathy

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Machine learning for human learners: opportunities, issues, tensions and threats

Educational Technology Research and Development ◽

10.1007/s11423-020-09858-2 ◽

2020 ◽

Author(s):

Mary E. Webb ◽

Andrew Fluck ◽

Johannes Magenheim ◽

Joyce Malyn-Smith ◽

Juliet Waters ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Ethical Issues ◽

Practical Experience ◽

Learning Systems ◽

Learning System ◽

Adaptive Behaviour ◽

Recent Developments ◽

Key Aspects ◽

School Curricula

AbstractMachine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

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