Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images

Jyoti, Kumari; Sushma, Sai; Kumar, Pawan; Yadav, Saurabh; Pachori, Ram Bilas; Mukherjee, Shaibal

doi:10.1016/j.compbiomed.2022.106331

Cited by 23 publications

(13 citation statements)

References 42 publications

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“…Then, by repeating its training through epochs, the accuracy of PRS improved with the training time. Figure 7b shows the training outcome after 10 successive epochs, with a maximum accuracy of 93.03% [76]. The results of potentiation and depression were then transformed into an artificial neural network to calculate the accuracy of a Modified National Institute of Standards and Technology (MNIST) handwritten data set.…”

Section: Resultsmentioning

confidence: 99%

Improved Resistive Switching Characteristics and Synaptic Functions of InZnO/SiO2 Bilayer Device

Ju,

Koo,

Kim

2023

Materials

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This paper investigates the bipolar resistive switching and synaptic characteristics of IZO single-layer and IZO/SiO2 bilayer two-terminal memory devices. The chemical properties and structure of the device with a SiO2 layer are confirmed by x-ray photoemission spectroscopy (XPS) and transmission electron microscopy (TEM) imaging. The device with the SiO2 layer showed better memory characteristics with a low current level, as well as better cell-to-cell and cycle-to-cycle uniformity. Moreover, the neuromorphic applications of the IZO/SiO2 bilayer device are demonstrated by pulse response. Paired pulse facilitation, excitatory postsynaptic current, and pulse-width-dependent conductance changes are conducted by the coexistence of short- and long-term memory characteristics. Moreover, Hebbian rules are emulated to mimic biological synapse function. The result of potentiation, depression, spike-rate-dependent plasticity, and spike-time-dependent plasticity prove their favorable abilities for future applications in neuromorphic computing architecture.

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Section: Resultsmentioning

confidence: 99%

Improved Resistive Switching Characteristics and Synaptic Functions of InZnO/SiO2 Bilayer Device

Ju,

Koo,

Kim

2023

Materials

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“…Lee and Lim’s approach outperforms the proposed model only in terms of accuracy, with a difference of 0.84%. Jyoti et al [11] achieved accuracies of 98.82% and 95.67% on small and large datasets respectively, which are less accurate than the proposed X-COVNet model. The approach proposed by Dalvi et al [12], where data preprocessing is performed using the Nearest-Neighbors interpolation technique, achieved 96.37%, 94.08%, and 98.89% for accuracy, precision, and recall, respectively, which tends to be less performing than the proposed approach.…”

Section: Discussionmentioning

confidence: 99%

X-COVNet: Externally Validated Model for Computer-Aided Diagnosis of Pneumonia-Like Lung Diseases in Chest X-Rays Based on Deep Transfer Learning

Pazos,

Gonzales,

Lorenzo

et al. 2024

Preprint

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Since the appearance of COVID-19, the accurate diagnosis of pneumonia-type lung diseases by chest radiographs has been a challenging task for experts, mainly due to the similarity of patterns between COVID-19 and viral or bacterial pneumonia. To address this challenge, a model for the computer-aided diagnosis of chest X-Rays has been developed in this research. This model might contribute to substantially increasing the accuracy of the diagnosis. This approach is based on supervised learning using neural networks, where the quality of the result depends on the quality of the dataset used during training. Image data augmentation techniques, hyperparameter adjustments and dropout layer contributed to achieve high performance values on test data in multi-class classification. The experiments conducted to evaluate the model yielded that it detects and classifies domain classes with an accuracy of 99.45% on training data, 99.27% on validation data and 99.06% on selected test data. The main contribution of this paper is a new Deep Convolutional Neural Network model using Deep Transfer Learning through the Xception architecture for the assisted diagnosis of COVID-19, pneumonia or healthy patients, trained on COVID-19 Chest X-Ray Database and evaluated through two external databases, which give the model novelty within the lack of external validation in all the literature reviewed.

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“…Jyoti, Kumari, et.al [10] proposed the TQWT parameters optimized for PSNR and SSIM yielded promising results. MCA-based model enabled efficient storage and classification using ResNet50 and Alex Net CNN models, achieving average accuracies of 98.82% and 94.64% for small and large datasets, respectively, surpassing conventional deep learning methods.…”

Section: Literature Surveymentioning

confidence: 99%

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

Suresh,

Meghana,

Sujitha

et al. 2024

TURCOMAT

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Medical image segmentation plays a crucial role in various clinical applications, facilitating accurate diagnosis and treatment planning. In communication systems, particularly telemedicine and remote healthcare monitoring, real-time processing and transmission of medical images are essential for timely diagnosis and intervention. Leveraging VLSI technology for implementing Kmeans offers a promising solution to address the computational demands of image segmentation while meeting the stringent requirements of communication systems. Existing systems often rely on software-based implementations such as threshold segmentation, leading to significant computational overhead and latency, particularly in resource-constrained environments. Moreover, these implementations struggle to achieve real-time performance, hindering their practical utility in communication systems for healthcare. So, this work proposed VLSI-based approach aims to overcome these challenges by offloading the computational burden from the software to hardware, enabling parallel processing and efficient utilization of resources. By exploiting the inherent parallelism of the K-means algorithm and optimizing hardware architecture, our design ensures high throughput and low latency, making it suitable for real-time medical image segmentation in communication systems.

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Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images

Cited by 23 publications

References 42 publications

Improved Resistive Switching Characteristics and Synaptic Functions of InZnO/SiO2 Bilayer Device

Improved Resistive Switching Characteristics and Synaptic Functions of InZnO/SiO2 Bilayer Device

X-COVNet: Externally Validated Model for Computer-Aided Diagnosis of Pneumonia-Like Lung Diseases in Chest X-Rays Based on Deep Transfer Learning

VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering

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