Bacterial Strain Classification using Convolutional Neural Network for Automatic Bacterial Disease Diagnosis

Trivedi, Sandeep; Patel, Nikhil; Faruqui, Nuruzzaman

doi:10.1109/confluence56041.2023.10048859

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…Although most previous works have adopted neural classification networks to determine whether a specific pathogen was present in an image ( Zhang et al., 2019 ; Cai et al., 2020 ; Kang et al., 2020 ; Khan et al., 2021 ; Momeny et al., 2022 ; Poomrittigul et al., 2022 ; Trivedi et al., 2023 ), we propose that it can achieve comparable or even better performance to adopt a detection network, rather than a classification network, in certain scenarios. This assertion is based on three reasons.…”

Section: Discussionmentioning

confidence: 99%

Clinical screening of Nocardia in sputum smears based on neural networks

Sun,

Xie,

Wang

et al. 2023

Front. Cell. Infect. Microbiol.

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ObjectiveNocardia is clinically rare but highly pathogenic in clinical practice. Due to the lack of Nocardia screening methods, Nocardia is often missed in diagnosis, leading to worsening the condition. Therefore, this paper proposes a Nocardia screening method based on neural networks, aiming at quick Nocardia detection in sputum specimens with low costs and thereby reducing the missed diagnosis rate.MethodsFirstly, sputum specimens were collected from patients who were infected with Nocardia, and a part of the specimens were mixed with new sputum specimens from patients without Nocardia infection to enhance the data diversity. Secondly, the specimens were converted into smears with Gram staining. Images were captured under a microscope and subsequently annotated by experts, creating two datasets. Thirdly, each dataset was divided into three subsets: the training set, the validation set and the test set. The training and validation sets were used for training networks, while the test set was used for evaluating the effeteness of the trained networks. Finally, a neural network model was trained on this dataset, with an image of Gram-stained sputum smear as input, this model determines the presence and locations of Nocardia instances within the image.ResultsAfter training, the detection network was evaluated on two datasets, resulting in classification accuracies of 97.3% and 98.3%, respectively. This network can identify Nocardia instances in about 24 milliseconds per image on a personal computer. The detection metrics of mAP50 on both datasets were 0.780 and 0.841, respectively.ConclusionThe Nocardia screening method can accurately and efficiently determine whether Nocardia exists in the images of Gram-stained sputum smears. Additionally, it can precisely locate the Nocardia instances, assisting doctors in confirming the presence of Nocardia.

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Section: Discussionmentioning

confidence: 99%

Clinical screening of Nocardia in sputum smears based on neural networks

Sun,

Xie,

Wang

et al. 2023

Front. Cell. Infect. Microbiol.

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“…This application processes the received data before forwarding it to the access point. Given the sensitive nature of healthcare-related data, our experiment employs a 4-way handshake using the Pre-Shared Key (PSK) scheme [35] . A Pairwise Master Key (PMK) is generated through 4096 iterations, resulting in a secure 256-bit key.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…AML attack: There is no Adversarial Machine Learning (AML) attack defense mechanism integrated with HAASNet [35] . Datasets related to the healthcare sector are valuable.…”

Section: Limitations and Future Scopementioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

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“…Applying Artificial Intelligence (AI)-driven solutions is a potential way to overcome this vulnerability [6]. A literature review has shown the effective application of AI, including in healthcare [7], robotics [8], microbiology [9], image segmentation [10], and road construction [11]. HAR is a subbranch of AI that has been applied in the proposed methodology to strengthen cyber-physical system security.…”

Section: Introductionmentioning

confidence: 99%

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

et al. 2023

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Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD 4.29±0.29 per month.

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Bacterial Strain Classification using Convolutional Neural Network for Automatic Bacterial Disease Diagnosis

Cited by 11 publications

References 29 publications

Clinical screening of Nocardia in sputum smears based on neural networks

Clinical screening of Nocardia in sputum smears based on neural networks

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

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