A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

Akgün, Devrim; Kabakuş, Abdullah Talha; Şentürk, Zehra Karapınar; Şentürk, A.; Kucukkulahli, Enver

doi:10.3906/elk-2105-64

Cited by 9 publications

(5 citation statements)

References 35 publications

(50 reference statements)

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“…By harnessing the computational capabilities of MobileNet, researchers can explore the intricate features of handwriting, including dynamic characteristics and spatial patterns, to identify distinctive markers associated with PD [54]. Additionally, the potential integration of MobileNet models with other modalities, such as speech signals, presents an opportunity to create comprehensive diagnostic frameworks that encompass multiple data sources, thereby enhancing the accuracy and reliability of PD diagnosis [55]. The future utilization of MobileNet models for PD diagnosis from handwriting offers a pathway towards innovative, technology-driven approaches that can revolutionize the early detection and management of neurodegenerative conditions, ultimately improving patient outcomes and quality of care.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Afandi,

Ardhianto,

Lin

2023

Preprint

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Neurodegenerative illnesses, such as Parkinson’s disease (PD), have a substantial impact on the overall well-being of those who are affected. This study investigates and contrasts the capabilities of convolutional neural networks (CNN) in detecting Parkinson’s disease (PD) by utilising hand-drawn images alongside wave and spiral images as input data. This study employs pre-trained CNN models, specifically MobileNet, ResNet50, EfficientNet-B1, and InceptionV3, to classify Parkinson’s disease (PD). The findings demonstrate that MobileNet surpasses other architectural designs, as evidenced by the F1-Score of the four classes: Spiral Normal (0.87), Spiral Parkinson (0.86), Wave Normal (0.97), and Wave Parkinson (0.97). MobileNet has also shown a remarkable accuracy of 0.92 in diagnosing Parkinson’s disease. The result demonstrates the efficacy of MobileNet in extracting features from images. The results of this study enhance the application of deep learning methods in the early detection of PD, as well as help indicate the effectiveness of patient therapy and exercise, promising better patient outcomes through timely intervention and treatment.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Afandi,

Ardhianto,

Lin

2023

Preprint

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“…The need for computerized analysis for fast and accurate diagnosis comes to the fore during this pandemic. Several works using automatic deep learning algorithms on CT scans [6][7][8][9][10] and machine learning algorithms on cough sounds [11][12][13][14][15][16][17][18][19][20][21][22] are proposed in literature. The works on CT scans [6][7][8][9][10] provide information about the degree of severity of the individual's lung damage.…”

Section: Related Workmentioning

confidence: 99%

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds

Ulukaya

Sarıca

Erdem

et al. 2023

Med Biol Eng Comput

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Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network–based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram as inputs and is abbreviated as MSCCov19Net . The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets. The Architecture of the Proposed MSCCov19Net Model for Covid-19 Detection

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“…DL is a methodology that has been proposed to perform classification and regression tasks with significant results in the area of data science [15,38]. DL algorithms are based on artificial neural networks to build computational models capable of extracting and learning the principal features of a data set.…”

Section: Deep Learning Frameworkmentioning

confidence: 99%

Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures

Cardenas

Gutiérrez

Aguilar-Ponce

2023

IJERPH

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Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system’s performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper.

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A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

Cited by 9 publications

References 35 publications

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds

Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures

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