An Overview of Convolutional Neural Network: Its Architecture and Applications

Sakib, Shadman; Ahmed, Nisar; Aj, Kabir; Ahmed, Hanaa Mohsin

doi:10.20944/preprints201811.0546.v4

Cited by 31 publications

(22 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Gated recurrent units (GRU) and long short-term memory (LSTM) are improved versions of RNN, and they provide state-of-the-art performance in many applications, including machine translation, speech recognition, and image captioning Abduh et al (2019) . Heart sound signals are sequential data with strong temporal correlation, so heart sound classification can be efficiently processed by RNN Nogueira et al (2019) ; Ismail et al (2022) ; Sakib et al (2019) . Figure 2 describes the Waveform representation of S1, S2, S3, and S4 sounds in systole and diastole intervals.…”

Section: Introductionmentioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

View full text Add to dashboard Cite

Heart sound classification plays a critical role in the early diagnosis of cardiovascular diseases. Although there have been many advances in heart sound classification in the last few years, most of them are still based on conventional segmented features and shallow structure-based classifiers. Therefore, we propose a new heart sound classification method based on improved mel-frequency cepstrum coefficient features and deep residual learning. Firstly, the heart sound signal is preprocessed, and its improved features are computed. Then, these features are used as input features of the neural network. The pathological information in the heart sound signal is further extracted by the deep residual network. Finally, the heart sound signal is classified into different categories according to the features learned by the neural network. This paper presents comprehensive analyses of different network parameters and network connection strategies. The proposed method achieves an accuracy of 94.43% on the dataset in this paper.

show abstract

Section: Introductionmentioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…These embeddings offer low-dimensionality data structures that reduce computational resources required for training while maximizing utility of information for learning. STR's final agents for Stratagem competitions 3 and 4 included both Convolutional Neural Networks (CNNs) based on state-of-the-art machine vision deep learning approaches [4] and Graph Convolutional Networks (GCNs) [5]. Both have performed well within the AFRL MIST network-like game topology and dynamics, with complementary strengths.…”

Section: System Overviewmentioning

confidence: 99%

Curriculum-heavy reinforcement learning for multi-domain operations

Pioch,

Sheldon,

Harris

et al. 2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

View full text Add to dashboard Cite

We present a framework for developing software agents via Machine Learning (ML) entitled Curriculum-Heavy Accelerated Learning in a Competitive Environment (CHALICE). CHALICE is designed to train and deploy intelligent agents capable of executing strategies for air-ground combat as embodied in AFRL's MIST turn-based wargame system. Such agents can be used to suggest courses of action in real-time to operational planners and to provide an adversarial opponent for evaluation of proposed courses of action. CHALICE uses state-of-the-art Deep Neural Networks (DNNs) to represent the state of the environment and Deep Reinforcement Learning (DRL) to train each agent via repeated feedback from outcomes of the MIST Stratagem game. Unlike recent DRL approaches for strategy games such as Go or StarCraft [1] [2], CHALICE minimizes dependence on existing corpora of human gameplay and trains efficiently with low computational resources and short convergence time (hours to days rather than weeks to months). Over the course of four government-led competitions, CHALICE produced agents that continually improved their performance, resulting in competitive play against human and automated opposing agents at relatively low training cost and time. In this paper, we motivate the operational problem and technical challenges, provide an overview of our technical approach, elaborate on our vision-based and graph-based DNN architecture design and agent training procedure, and present results from the most recent Stratagem competition. We close with a discussion of future research recommendations.

show abstract

“…As a result, CNNs are designed to enable computers to see the world in accordance with human perception. Natural language processing, image classification, and image recognition can all be performed using CNNs in this way [ 31 ]. The CNN is a type of DNN that often contains a convolutional layer, activation layer (a nonlinear activation layer), pooling layer, fully-connected layer, and output layer [ 32 , 33 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

Ahmed

Mstafa

2022

Diagnostics

View full text Add to dashboard Cite

Recently, many diseases have negatively impacted people’s lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, and low-cost computer-based tools for the early prediction of knee OA patients are urgently needed. In this paper, as part of addressing this issue, we developed a new method to efficiently diagnose and classify knee osteoarthritis severity based on the X-ray images to classify knee OA in (i.e., binary and multiclass) in order to study the impact of different class-based, which has not yet been addressed in previous studies. This will provide physicians with a variety of deployment options in the future. Our proposed models are basically divided into two frameworks based on applying pre-trained convolutional neural networks (CNN) for feature extraction as well as fine-tuning the pre-trained CNN using the transfer learning (TL) method. In addition, a traditional machine learning (ML) classifier is used to exploit the enriched feature space to achieve better knee OA classification performance. In the first one, we developed five classes-based models using a proposed pre-trained CNN for feature extraction, principal component analysis (PCA) for dimensionality reduction, and support vector machine (SVM) for classification. While in the second framework, a few changes were made to the steps in the first framework, the concept of TL was used to fine-tune the proposed pre-trained CNN from the first framework to fit the two classes, three classes, and four classes-based models. The proposed models are evaluated on X-ray data, and their performance is compared with the existing state-of-the-art models. It is observed through conducted experimental analysis to demonstrate the efficacy of the proposed approach in improving the classification accuracy in both multiclass and binary class-based in the OA case study. Nonetheless, the empirical results revealed that the fewer multiclass labels used, the better performance achieved, with the binary class labels outperforming all, which reached a 90.8% accuracy rate. Furthermore, the proposed models demonstrated their contribution to early classification in the first stage of the disease to help reduce its progression and improve people’s quality of life.

show abstract

An Overview of Convolutional Neural Network: Its Architecture and Applications

Cited by 31 publications

References 15 publications

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Curriculum-heavy reinforcement learning for multi-domain operations

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

Contact Info

Product

Resources

About