Optimized Transfer Learning Based Dementia Prediction System for Rehabilitation Therapy Planning DOI Creative Commons
Ping‐Huan Kuo,

Chen-Ting Huang,

Ting-Chun Yao

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2023, Volume and Issue: 31, P. 2047 - 2059

Published: Jan. 1, 2023

Dementia is a neurodegenerative disease that causes progressive deterioration of thinking, memory, and the ability to perform daily tasks. Other common symptoms include emotional disorders, language reduced mobility; however, self-consciousness unaffected. irreversible, medicine can only slow but not stop degeneration. However, if dementia could be predicted, its onset may preventable. Thus, this study proposes revolutionary transfer-learning machine-learning model predict from magnetic resonance imaging data. In training, k-fold cross-validation various parameter optimization algorithms were used increase prediction accuracy. Synthetic minority oversampling was for data augmentation. The final achieved an accuracy 90.7%, superior competing methods on same set. This study's facilitates early diagnosis dementia, which key arresting neurological disease, useful underserved regions where many do have access human physician. future, proposed system plan rehabilitation therapy programs patients.

Language: Английский

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks DOI Creative Commons
Saeed Iqbal, Adnan N. Qureshi, Jianqiang Li

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(5), P. 3173 - 3233

Published: April 4, 2023

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection video Speech Recognition. CNN is a special type of Neural Network, which compelling effective learning ability to learn features at several steps during augmentation the data. Recently, interesting inspiring ideas Deep Learning (DL) such as activation functions, hyperparameter optimization, regularization, momentum loss functions improved performance, operation execution Different internal architecture innovation representational style significantly performance. This survey focuses taxonomy deep learning, models vonvolutional network, depth width in addition components, applications current challenges learning.

Language: Английский

Citations

87
Computer-aided breast cancer detection and classification in mammography: A comprehensive review DOI Creative Commons
Kosmia Loizidou, Rafaella Elia, Costas Pitris

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 153, P. 106554 - 106554

Published: Jan. 13, 2023

Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% all new cases worldwide, making major morbidity mortality. Mammography an effective screening tool early detection management breast cancer. However, identification interpretation lesions challenging even expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists accurately detect and/or classify This review examines recent literature on automatic classification in mammograms, using both conventional feature-based machine learning deep algorithms. The begins with comparison algorithms specifically two types abnormalities, micro-calcifications masses, followed by use sequential mammograms improving performance available Food Drug Administration (FDA) approved CAD related triage diagnosis subsequently presented. Finally, description open access mammography datasets provided potential opportunities future work this field highlighted. comprehensive here can serve thorough introduction but also provide indicative directions guide applications.

Language: Английский

Citations

78
AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions DOI Open Access
Navneet Vinod Melarkode, Kathiravan Srinivasan, Saeed Mian Qaisar

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(4), P. 1183 - 1183

Published: Feb. 13, 2023

Skin cancer continues to remain one of the major healthcare issues across globe. If diagnosed early, skin can be treated successfully. While early diagnosis is paramount for an effective cure cancer, current process requires involvement specialists, which makes it expensive procedure and not easily available affordable in developing countries. This dearth specialists has given rise need develop automated systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems assist detection consequently lower its morbidity, and, turn, alleviate mortality rate associated with it. Machine learning deep are branches AI that deal statistical modeling inference, progressively learn from data fed into them predict desired objectives characteristics. survey focuses on Learning Deep techniques deployed field diagnosis, while maintaining a balance between both techniques. A comparison made widely used datasets prevalent review papers, discussing diagnosis. The study also discusses insights lessons yielded by prior works. culminates future direction scope, will subsequently help addressing challenges faced within

Language: Английский

Citations

67
Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension DOI

Xiao-Ming Yu,

Wenxiang Qin,

Xiao Lin

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107408 - 107408

Published: Aug. 29, 2023

Language: Английский

Citations

47
An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study DOI Creative Commons
Munish Khanna, Law Kumar Singh,

Kapil Shrivastava

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(5), P. e26799 - e26799

Published: Feb. 28, 2024

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These support healthcare professionals like radiologists their decision-making process efficiently detecting abnormalities as well offering accurate dependable information. heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. can subsequently assist related medical conditions. The task identifying patterns biomedical data be quite challenging due presence numerous irrelevant or redundant features. Therefore, it is crucial propose then utilize feature (FS) order eliminate these primary goal FS approaches improve accuracy classification eliminating that are less informative. phase plays critical attaining optimal results machine learning (ML)-driven CAD systems. effectiveness ML models significantly enhanced incorporating during training phase. This empirical study presents methodology for using technique. proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding (EHO), hybrid algorithm two. algorithms were previously employed; however, addressing issues predicting human diseases has not been investigated. following evaluation focuses categorization benign malignant tumours publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. five-fold cross-validation technique employed mitigate risk over-fitting. approach's proficiency determined based several metrics, including sensitivity, specificity, precision, accuracy, area under receiver-operating characteristic curve (AUC), F1-score. best value computed through suggested 97.96%. clinical decision system demonstrates highly favourable performance outcome, making valuable tool practitioners secondary opinion reducing overburden expert practitioners.

Language: Английский

Citations

23
Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers DOI Creative Commons
Bernardo Pereira Cabral, Luiza Amara Maciel Braga, Shabbir Syed-Abdul

et al.

Current Oncology, Journal Year: 2023, Volume and Issue: 30(3), P. 3432 - 3446

Published: March 16, 2023

Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains oncology. However, potential AI and barriers its widespread adoption remain unclear. This study aimed address gap by conducting a cross-sectional, global, web-based survey over 1000 cancer researchers. The results indicated that most respondents believed would positively impact grading classification, follow-up services, diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating into clinical practice lack standardization health data. These pose significant challenges, particularly regarding testing, validation, certification, auditing algorithms systems. provide valuable insights for informed decision-making stakeholders involved research development, individual researchers funding agencies.

Language: Английский

Citations

27
Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review DOI Creative Commons
Weichu Tao, Qian Sun, Bingxiang Xu

et al.

Life, Journal Year: 2025, Volume and Issue: 15(2), P. 283 - 283

Published: Feb. 12, 2025

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, biomarkers evaluating response a patient is key for application immunotherapy in wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, DNA methylation have been proved be sensitive accurate predictors immunotherapy. A large number predictive models based on these features, utilizing traditional machine learning or deep frameworks, also proposed. In this review, we aim cover recent advances predicting using features. These include new measurements, research cohorts, data sources, models. Key findings emphasize importance TMB, neoantigens, MSI, mutational signatures ICI responses. The integration bulk single-cell RNA sequencing enhanced our understanding microenvironment enabled identification like PD-L1 IFN-γ signatures. Public datasets improved tools. However, challenges remain, need diverse clinical datasets, standardization data, model interpretability. Future will require collaboration among researchers, clinicians, scientists address issues enhance cancer precision.

Language: Английский

Citations

1
A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics DOI
Hari Mohan, Joon Yoo

Journal of Cancer Research and Clinical Oncology, Journal Year: 2023, Volume and Issue: 149(15), P. 14365 - 14408

Published: Aug. 4, 2023

Language: Английский

Citations

22
A review on lung disease recognition by acoustic signal analysis with deep learning networks DOI Creative Commons
Alyaa Hamel Sfayyih, Nasri Sulaiman, Ahmad H. Sabry

et al.

Journal Of Big Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: June 12, 2023

Abstract Recently, assistive explanations for difficulties in the health check area have been made viable thanks considerable portion to technologies like deep learning and machine learning. Using auditory analysis medical imaging, they also increase predictive accuracy prompt early disease detection. Medical professionals are thankful such technological support since it helps them manage further patients because of shortage skilled human resources. In addition serious illnesses lung cancer respiratory diseases, plurality breathing is gradually rising endangering society. Because prediction immediate treatment crucial disorders, chest X-rays sound audio proving be quite helpful together. Compared related review studies on classification/detection using algorithms, only two based signal diagnosis conducted 2011 2018. This work provides a recognition with acoustic networks. We anticipate that physicians researchers working sound-signal-based will find this material beneficial.

Language: Английский

Citations

19
A comprehensive review of extreme learning machine on medical imaging DOI
Yoleidy Huérfano, Marco Mora, Karina Vilches-Ponce

et al.

Neurocomputing, Journal Year: 2023, Volume and Issue: 556, P. 126618 - 126618

Published: July 29, 2023

Language: Английский

Citations

17