Evaluation of Deep-Learning Feature Based COVID-19 Classifier in Various Neural Network

We have developed a novel photoacoustic microscopy/ultrasound (PAM/US) endoscope to image post-treatment rectal cancer for surgical management of residual tumor after radiation and chemotherapy. Paired with a deep-learning convolutional neural network (CNN), the PAM images accurately differentiated pathological complete responders (pCR) from incomplete responders. However, the role of CNNs compared with traditional histogram-feature based classifiers needs further exploration. In this work, we compare the performance of the CNN models to generalized linear models (GLM) across 24 ex vivo specimens and 10 in vivo patient examinations. First order statistical features were extracted from histograms of PAM and US images to train, validate and test GLM models, while PAM and US images were directly used to train, validate, and test CNN models. The PAM-CNN model performed superiorly with an AUC of 0.96 (95% CI: 0.95-0.98) compared to the best PAM-GLM model using kurtosis with an AUC of 0.82 (95% CI: 0.82-0.83). We also found that both CNN and GLMs derived from photoacoustic data outperformed those utilizing ultrasound alone. We conclude that deep-learning neural networks paired with photoacoustic images is the optimal analysis framework for determining presence of residual cancer in the treated human rectum.

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Different molecular enumeration influences in deep learning: an example using aqueous solubility

Briefings in Bioinformatics ◽

10.1093/bib/bbaa092 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jen-Hao Chen ◽

Yufeng Jane Tseng

Keyword(s):

Neural Network ◽

Deep Learning ◽

Single Molecule ◽

Chemical Structure ◽

Molecular Graph ◽

Aqueous Solubility ◽

Solubility Prediction ◽

Input Line ◽

Biological Phenomena ◽

Feature Based

Abstract Aqueous solubility is the key property driving many chemical and biological phenomena and impacts experimental and computational attempts to assess those phenomena. Accurate prediction of solubility is essential and challenging, even with modern computational algorithms. Fingerprint-based, feature-based and molecular graph-based representations have all been used with different deep learning methods for aqueous solubility prediction. It has been clearly demonstrated that different molecular representations impact the model prediction and explainability. In this work, we reviewed different representations and also focused on using graph and line notations for modeling. In general, one canonical chemical structure is used to represent one molecule when computing its properties. We carefully examined the commonly used simplified molecular-input line-entry specification (SMILES) notation representing a single molecule and proposed to use the full enumerations in SMILES to achieve better accuracy. A convolutional neural network (CNN) was used. The full enumeration of SMILES can improve the presentation of a molecule and describe the molecule with all possible angles. This CNN model can be very robust when dealing with large datasets since no additional explicit chemistry knowledge is necessary to predict the solubility. Also, traditionally it is hard to use a neural network to explain the contribution of chemical substructures to a single property. We demonstrated the use of attention in the decoding network to detect the part of a molecule that is relevant to solubility, which can be used to explain the contribution from the CNN.

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Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

Applied Sciences ◽

10.3390/app112411595 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11595

Author(s):

Abdolmaged Alkhulaifi ◽

Arshad Jamal ◽

Irfan Ahmad

Keyword(s):

Neural Network ◽

Deep Learning ◽

Transportation Network ◽

Learning Model ◽

Observation Angle ◽

Traffic Sign ◽

Traffic Signs ◽

Field Surveys ◽

Feature Based ◽

Deep Learning Model

Traffic signs are essential for the safe and efficient movement of vehicles through the transportation network. Poor sign visibility can lead to accidents. One of the key properties used to measure the visibility of a traffic sign is retro-reflection, which indicates how much light a traffic sign reflects back to the driver. The retro-reflection of the traffic sign degrades over time until it reaches a point where the traffic sign has to be changed or repaired. Several studies have explored the idea of modeling the sign degradation level to help the authorities in effective scheduling of sign maintenance. However, previous studies utilized simpler models and proposed multiple models for different combinations of the sheeting type and color used for the traffic sign. In this study, we present a neural network based deep learning model for traffic sign retro-reflectivity prediction. Data utilized in this study was collected using a handheld retro-reflectometer GR3 from field surveys of traffic signs. Sign retro-reflective measurements (i.e., the RA values) were taken for different sign sheeting brands, grades, colors, orientation angles, observation angles, and aging periods. Feature-based sensitivity analysis was conducted to identify variables’ relative importance in determining retro-reflectivity. Results show that the sheeting color and observation angle were the most significant variables, whereas sign orientation was the least important. Considering all the features, RA prediction results obtained from one-hot encoding outperformed other models reported in the literature. The findings of this study demonstrate the feasibility and robustness of the proposed neural network based deep learning model in predicting the sign retro-reflectivity.

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A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/ijaeis.2020040104 ◽

2020 ◽

Vol 11 (2) ◽

pp. 44-57

Author(s):

Shamik Tiwari

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Texture Features ◽

Classification Performance ◽

Learning Approaches ◽

Learning Framework ◽

Classification Framework ◽

Feature Based

The classification of plants is one of the most important aims for botanists since plants have a significant part in the natural life cycle. In this work, a leaf-based automatic plant classification framework is investigated. The aim is to compare two different deep learning approaches named Deep Neural Network (DNN) and deep Convolutional Neural Network (CNN). In the case of deep neural network, hybrid shapes and texture features are utilized as hand-crafted features while in the case of the convolution non-handcraft, features are applied for classification. The offered frameworks are evaluated with a public leaf database. From the simulation results, it is confirmed that the deep CNN-based deep learning framework demonstrates superior classification performance than the handcraft feature based approach.

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COMPARISON OF THE PRECISION OF OPTICAL DIFFERENTATION BETWEEN NEOPLASTIC AND NON-NEOPLASTIC SUBCENTRIMETRIC POLYP HISTOLOGY BY ENDOSCOPIC EXPERTS AND ARTEFICIAL INTELLIGENCE DEEP LEARNING NEURAL NETWORK (POLYPBRAIN)

10.1055/s-0040-1704154 ◽

2020 ◽

Author(s):

L Madácsy ◽

M Szalai ◽

Á Finta ◽

K Zsobrák ◽

L Oczella ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Learning Neural Network

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Developing an Artificial Intelligence Project in your Radiology Department

Indian Journal of Musculoskeletal Radiology ◽

10.25259/ijmsr_50_2019 ◽

2020 ◽

Vol 2 ◽

pp. 58-61 ◽

Cited By ~ 1

Author(s):

Syed Junaid ◽

Asad Saeed ◽

Zeili Yang ◽

Thomas Micic ◽

Rajesh Botchu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Health Care ◽

Deep Learning ◽

Learning Algorithms ◽

Radiology Department ◽

Short Article ◽

Computing Power ◽

Road Map ◽

Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

10.26434/chemrxiv.8150666.v1 ◽

2019 ◽

Author(s):

Seoin Back ◽

Junwoong Yoon ◽

Nianhan Tian ◽

Wen Zhong ◽

Kevin Tran ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

High Throughput ◽

High Throughput Screening ◽

Binding Energies ◽

Surface Structures ◽

Voronoi Polyhedra ◽

Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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