Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges DOI Creative Commons
Muhammad Waqar Azeem, Shumaila Javaid, Ruhul Amin Khalil

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(7), P. 850 - 850

Published: July 18, 2023

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment care has increased in popularity enhanced patient safety quality care. Therefore, this paper reviews the critical role ANNs providing valuable insights patients’ healthcare efficient disease diagnosis. We study different types existing literature that advance ANNs’ adaptation complex applications. Specifically, we investigate advances predicting viral, cancer, skin, COVID-19 diseases. Furthermore, propose deep convolutional network (CNN) model called ConXNet, based on chest radiography images, improve detection accuracy disease. ConXNet is trained tested using image dataset obtained from Kaggle, achieving more than 97% 98% precision, which better other state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, COVID-Net, having 93.1%, 94.10%, 84.76%, 90% 94%, 95%, 85%, 92% respectively. The results show performed significantly well relatively compared with aforementioned models. Moreover, reduces time complexity by dropout layers batch normalization techniques. Finally, highlight future research directions challenges, algorithms, insufficient available data, privacy security, integration biosensing ANNs. These require considerable attention improving scope diagnostic

Language: Английский

An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis DOI
Hoda Zamani, Mohammad H. Nadimi-Shahraki

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 90, P. 105879 - 105879

Published: Jan. 1, 2024

Language: Английский

Citations

28
A Cuproptosis Activation Scoring model predicts neoplasm-immunity interactions and personalized treatments in glioma DOI Creative Commons
Bo Chen,

Xiaoxi Zhou,

Liting Yang

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 148, P. 105924 - 105924

Published: Aug. 8, 2022

Gliomas are malignant tumors in the central nervous system. Cuproptosis is a newly discovered cell death mechanism targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that cuproptosis participates tumor progression, but its role gliomas still elusive. Here, we systematically explored bulk-tumor and single-cell transcriptome data to reveal gliomas. The activity score (CuAS) was constructed based on cuproptosis-related genes, machine learning techniques validated stability. High CuAS were more likely poor prognosis an aggressive mesenchymal (MES) subtype. Subsequently, SCENIC algorithm predicted 20 CuAS-related transcription factors (TFs) Function enrichment microenvironment analyses associated with immune infiltration. Accordingly, intercellular communications between neoplasm immunity by R package "Cellchat". Five signaling pathways 8 ligand-receptor pairs including ICAM1, ITGAX, ITGB2, ANXA1-FRR1, like, identified suggest how connected neoplastic cells. Critically, 13 potential drugs high CuAs according CTRP PRISM databases, oligomycin A, dihydroartemisinin, others. Taken together, involved glioma aggressiveness, neoplasm-immune interactions, may be used assist drug selection.

Language: Английский

Citations

55
RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames DOI
Mobeen Ur Rehman, Jihyoung Ryu, Imran Fareed Nizami

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 152, P. 106426 - 106426

Published: Dec. 20, 2022

Language: Английский

Citations

50
Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation DOI Open Access
Saroj Kumar Sahoo, Essam H. Houssein, M. Premkumar

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 227, P. 120367 - 120367

Published: May 6, 2023

Language: Английский

Citations

37
PPNet: Pyramid pooling based network for polyp segmentation DOI
Ke‐Li Hu,

Wenping Chen,

YuanZe Sun

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 160, P. 107028 - 107028

Published: May 10, 2023

Language: Английский

Citations

30
Deep Learning and Optimization-Based Methods for Skin Lesions Segmentation: A Review DOI Creative Commons
Khalid M. Hosny,

Doaa Elshoura,

Ehab R. Mohamed

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 85467 - 85488

Published: Jan. 1, 2023

Skin cancer is a senior public health issue that could profit from computer-aided diagnosis to decrease the encumbrance of this widespread disease. Researchers have been more motivated develop systems because visual examination wastes time. The initial stage in skin lesion analysis segmentation, which might assist following categorization task. It difficult task sometimes whole be same colors, and borders pigment regions can foggy. Several studies effectively handled segmentation; nevertheless, developing new methodologies improve efficiency necessary. This work thoroughly analyzes most advanced algorithms methods for segmentation. review begins with traditional segmentation techniques, followed by brief using deep learning optimization techniques. main objective highlight strengths weaknesses wide range algorithms. Additionally, it examines various commonly used datasets lesions metrics evaluate performance these

Language: Английский

Citations

27
A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images DOI Open Access
Haozhe Jia, Haoteng Tang, Guixiang Ma

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 155, P. 106698 - 106698

Published: Feb. 22, 2023

Language: Английский

Citations

23
CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration DOI Creative Commons
Mohamed Abdel‐Basset, Reda Mohamed, Ibrahim Alrashdi

et al.

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 10, 2024

Abstract Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists thought of combining chest X-ray (CXR) images with deep learning techniques rapidly detect people infected COVID-19 or any other disease. Image segmentation as preprocessing step has an essential role in improving performance techniques, it could separate most relevant features better train techniques. several approaches were proposed tackle image problem accurately. Among methods, multilevel thresholding-based methods won significant interest due their simplicity, accuracy, relatively low storage requirements. However, increasing threshold levels, traditional failed achieve segmented reasonable amount time. researchers recently used metaheuristic algorithms this problem, but existing still suffer from slow convergence speed stagnation into local minima number levels increases. study presents alternative technique based on enhanced version Kepler optimization algorithm (KOA), namely IKOA, segment CXR at small, medium, high levels. Ten are assess IKOA ten (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, T-30). To observe its effectiveness, is compared terms indicators. The experimental outcomes disclose superiority over all algorithms. Furthermore, IKOA-based eight different newly CNN model called CNN-IKOA find out effectiveness step. Five indicators, overall precision, recall, F1-score, specificity, CNN-IKOA’s effectiveness. CNN-IKOA, according outcomes, outstanding for where reach 94.88% 96.57% 95.40% recall.

Language: Английский

Citations

11
Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images DOI Open Access

Jyoti Kumari,

S Sushma,

Saurabh Yadav

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 152, P. 106331 - 106331

Published: Nov. 24, 2022

Language: Английский

Citations

38
An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer DOI
Wei Zhu, Lei Liu,

Fangjun Kuang

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 151, P. 106227 - 106227

Published: Oct. 21, 2022

Language: Английский

Citations

35