Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images

Ukwuoma, Chiagoziem C.; Cai, Dongsheng; Heyat, Md Belal Bin; Bamisile, Olusola; Adun, Humphrey; Al‐Huda, Zaid; Al-antari, Mugahed A.

doi:10.1016/j.jksuci.2023.101596

Cited by 30 publications

(5 citation statements)

References 74 publications

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“…In biology, the analytical and predictive capabilities of AI models have been empirically demonstrated across multiple research directions ( Ukwuoma et al, 2022b ; 2022a ; 2023 ; Fazmiya et al, 2024 ; Heyat et al, 2024 ; Sumbul et al, 2024 ). Based on this, we propose an innovative hybrid neural network model, STM-ac4C, designed to accurately identify N4-acetylcytidine (ac4C) modification sites on human mRNA.…”

Section: Methodsmentioning

confidence: 99%

STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention

Yi,

Zhou,

Deng

2024

Front. Genet.

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N4-acetylcysteine (ac4C) is a chemical modification in mRNAs that alters the structure and function of mRNA by adding an acetyl group to the N4 position of cytosine. Researchers have shown that ac4C is closely associated with the occurrence and development of various cancers. Therefore, accurate prediction of ac4C modification sites on human mRNA is crucial for revealing its role in diseases and developing new diagnostic and therapeutic strategies. However, existing deep learning models still have limitations in prediction accuracy and generalization ability, which restrict their effectiveness in handling complex biological sequence data. This paper introduces a deep learning-based model, STM-ac4C, for predicting ac4C modification sites on human mRNA. The model combines the advantages of selective kernel convolution, temporal convolutional networks, and multi-head self-attention mechanisms to effectively extract and integrate multi-level features of RNA sequences, thereby achieving high-precision prediction of ac4C sites. On the independent test dataset, STM-ac4C showed improvements of 1.81%, 3.5%, and 0.37% in accuracy, Matthews correlation coefficient, and area under the curve, respectively, compared to the existing state-of-the-art technologies. Moreover, its performance on additional balanced and imbalanced datasets also confirmed the model’s robustness and generalization ability. Various experimental results indicate that STM-ac4C outperforms existing methods in predictive performance. In summary, STM-ac4C excels in predicting ac4C modification sites on human mRNA, providing a powerful new tool for a deeper understanding of the biological significance of mRNA modifications and cancer treatment. Additionally, the model reveals key sequence features that influence the prediction of ac4C sites through sequence region impact analysis, offering new perspectives for future research. The source code and experimental data are available at https://github.com/ymy12341/STM-ac4C.

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Section: Methodsmentioning

confidence: 99%

STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention

Yi,

Zhou,

Deng

2024

Front. Genet.

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“…This provides a form of visual explanation alongside the LLM’s textual report. The combined power of detailed textual explanations and visual heat maps can empower healthcare professionals in several ways: It improves diagnostic accuracy by offering a comprehensive understanding of the LLM’s reasoning; It facilitates communication with patients by providing clear and accessible explanations of the diagnostic process [ 16 , 17 ]; It fosters collaboration between humans and machines, allowing medical professionals to leverage the strengths of LLMs while retaining their own irreplaceable role in healthcare decision making [ 15 ]. …”

Section: Future Directionsmentioning

confidence: 99%

“…It facilitates communication with patients by providing clear and accessible explanations of the diagnostic process [ 16 , 17 ];…”

Section: Future Directionsmentioning

confidence: 99%

“…(1) It improves diagnostic accuracy by offering a comprehensive understanding of the LLM's reasoning; (2) It facilitates communication with patients by providing clear and accessible explanations of the diagnostic process [16,17]; (3) It fosters collaboration between humans and machines, allowing medical professionals to leverage the strengths of LLMs while retaining their own irreplaceable role in healthcare decision making [15].…”

Section: Future Directionsmentioning

confidence: 99%

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Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis

Al-antari

2024

Diagnostics

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Rapid advancements in artificial intelligence (AI) and machine learning (ML) are currently transforming the field of diagnostics, enabling unprecedented accuracy and efficiency in disease detection, classification, and treatment planning. This Special Issue, entitled “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis”, presents a curated collection of cutting-edge research that explores the integration of AI and ML technologies into various diagnostic modalities. The contributions presented here highlight innovative algorithms, models, and applications that pave the way for improved diagnostic capabilities across a range of medical fields, including radiology, pathology, genomics, and personalized medicine. By showcasing both theoretical advancements and practical implementations, this Special Issue aims to provide a comprehensive overview of current trends and future directions in AI-driven diagnostics, fostering further research and collaboration in this dynamic and impactful area of healthcare. We have published a total of 12 research articles in this Special Issue, all collected between March 2023 and December 2023, comprising 1 Editorial cover letter, 9 regular research articles, 1 review article, and 1 article categorized as “other”.

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“…The use of chest X-ray images to gain an understanding of different ailments kinds has generated significant attention among physicians and radiologists due to the potential for automated assessment enabled by deep learning networks [59,60]. As a result, the issue of safeguarding the integrity of these models has emerged as a matter of utmost importance.…”

Section: A Medical Image Adversarial Attack and Defense Classificatio...mentioning

confidence: 99%

A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

Muoka,

Yi,

Ukwuoma

et al. 2023

Mathematics

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Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.

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Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images

Cited by 30 publications

References 74 publications

STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention

STM-ac4C: a hybrid model for identification of N4-acetylcytidine (ac4C) in human mRNA based on selective kernel convolution, temporal convolutional network, and multi-head self-attention

Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis

A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

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