Edge deep learning in computer vision and medical diagnostics: a comprehensive survey DOI Creative Commons
Yiwen Xu,

Tariq Khan,

Yang Song

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(3)

Published: Jan. 17, 2025

Edge deep learning, a paradigm change reconciling edge computing and facilitates real-time decision making attuned to environmental factors through the close integration of computational resources data sources. Here we provide comprehensive review current state art in focusing on computer vision applications, particular medical diagnostics. An overview foundational principles technical advantages learning is presented, emphasising capacity this technology revolutionise wide range domains. Furthermore, present novel categorisation hardware platforms based performance usage scenarios, facilitating platform selection operational effectiveness. Following this, dive into approaches effectively implement neural networks devices, encompassing methods such as lightweight design model compression. Reviewing practical applications fields general diagnostics particular, demonstrate profound impact edge-deployed models can have real-life situations. Finally, an analysis potential future directions obstacles adoption with intention stimulate further investigations advancements intelligent solutions. This survey provides researchers practitioners reference shedding light critical role plays advancement applications.

Language: Английский

Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries DOI Creative Commons
Azeez Okikiola Lawal, Tolulope Joseph Ogunniyi, Oriire Idunnuoluwa Oludele

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 8, 2025

Abstract Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 deaths estimated by 2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the use of biomarkers that play crucial roles risk assessment, therapy selection, disease monitoring. Tumor histology, single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, diagnostics emerged as pivotal tools for detection. The integration artificial intelligence, deep learning convolutional neural networks, has enhanced accuracy data analysis capabilities. However, developing countries face significant challenges including financial constraints, inadequate healthcare infrastructure, limited access to advanced technologies. impact COVID-19 further complicated management resource-limited settings. Future research should focus on precision medicine early diagnosis sophisticated laboratory techniques improve prognosis outcomes. This review examines evolving landscape detection, focusing breakthroughs limitations countries, while providing recommendations advancing tumor resource-constrained environments.

Language: Английский

Citations

1
Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues DOI Creative Commons
Muhammad Ali, Viviana Benfante,

Ghazal Basirinia

et al.

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(2), P. 59 - 59

Published: Feb. 15, 2025

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer and immunology, with object detection, feature extraction, classification, segmentation applications. Advancements in deep learning (DL) research have been a critical factor advancing computer techniques for mining. A significant improvement the accuracy of detection algorithms has achieved result emergence open-source software innovative neural network architectures. Automated now enables extraction quantifiable cellular spatial features from microscope images cells tissues, providing insights into organization various diseases. This review aims to examine latest AI DL mining microscopy images, aid biologists who less background knowledge machine (ML), incorporate ML models focus images.

Language: Английский

Citations

1
Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis DOI Creative Commons
Bitao Jiang, Lingling Bao,

Songqin He

et al.

Breast Cancer Research, Journal Year: 2024, Volume and Issue: 26(1)

Published: Sept. 20, 2024

Language: Английский

Citations

8
Vision transformer promotes cancer diagnosis: A comprehensive review DOI
Xiaoyan Jiang, Shuihua Wang‎, Yudong Zhang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 252, P. 124113 - 124113

Published: May 1, 2024

Language: Английский

Citations

7
Cross Branch Co-Attention Network multimodal models based on Raman and FTIR spectroscopy for diagnosis of multiple selected cancers DOI

Xuguang Zhou,

Chen Chen,

Enguang Zuo

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 166, P. 112204 - 112204

Published: Sept. 6, 2024

Language: Английский

Citations

7
Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach DOI Creative Commons
Eid Albalawi, Arastu Thakur,

D. Ramya Dorai

et al.

Frontiers in Computational Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: June 12, 2024

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes susceptibility to human error.

Language: Английский

Citations

6
Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review DOI Open Access

Yousaku Ozaki,

P M Broughton,

Hamed Abdollahi

et al.

Published: June 11, 2024

Cancer is one of the leading causes death, making timely diagnosis and prognosis very important. Utilization AI (artificial intelligence) enables providers to organize process patient data in a way that can lead better overall outcomes. This review paper aims look at varying uses for clinical utility. PubMed EBSCO databases were utilized finding publications from January 1, 2013, December 22, 2023. Articles collected using key search terms such as “artificial intelligence” “machine learning.” Included collection studies application determining cancer multi-omics data, radiomics, pathomics, laboratory data. The resulting 89 categorized into eight sections based on type then further subdivided two subsections focusing prognosis, respectively. 8 integrated more than form omics, namely genomics, transcriptomics, epigenomics, proteomics. Incorporating alongside omics represents significant advancement. Given considerable potential this domain, ongoing prospective are essential enhance algorithm interpretability ensure safe integration.

Language: Английский

Citations

5
Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives DOI Creative Commons
Vishnu Priya Veeraraghavan,

Shikhar Daniel,

Arun Kumar Dasari

et al.

Oral Oncology Reports, Journal Year: 2024, Volume and Issue: 11, P. 100591 - 100591

Published: June 29, 2024

Artificial intelligence (AI) has emerged as a promising tool in oral oncology, particularly the field of prediction. This review provides comprehensive outlook on role AI predicting cancer, covering key aspects such data collection and preprocessing, machine learning techniques, performance evaluation validation, challenges, future prospects, implications for clinical practice. Various algorithms, including supervised learning, unsupervised deep approaches, have been discussed context cancer Additionally, challenges interpretability, accessibility, regulatory compliance, legal are addressed along with research directions potential impact oncology care.

Language: Английский

Citations

5
A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models DOI Creative Commons
Chrysovalantis Voutouri,

Demetris Englezos,

Constantinos Zamboglou

et al.

Communications Medicine, Journal Year: 2024, Volume and Issue: 4(1)

Published: Oct. 17, 2024

In the era of personalized cancer treatment, understanding intrinsic heterogeneity tumors is crucial. Despite some patients responding favorably to a particular others may not benefit, leading varied efficacy observed in standard therapies. This study focuses on prediction tumor response chemo-immunotherapy, exploring potential mechanics and medical imaging as predictive biomarkers. We have extensively studied "desmoplastic" tumors, characterized by dense very stiff stroma, which presents substantial challenge for treatment. The increased stiffness such can be restored through pharmacological intervention with mechanotherapeutics. developed deep learning methodology based shear wave elastography (SWE) images, involved convolutional neural network (CNN) model enhanced attention modules. was evaluated biomarker setting detecting responsive, stable, non-responsive chemotherapy, immunotherapy, or combination, following mechanotherapeutics administration. A dataset 1365 SWE images obtained from 630 our previous experiments used train successfully evaluate methodology. combination models, has demonstrated promising results disease diagnosis classification but their predicting prior therapy yet fully realized. present strong evidence that integrating SWE-derived biomarkers automatic segmentation algorithms enables accurate detection therapeutic outcomes. approach enhance treatment providing non-invasive, reliable predictions Voutouri, Englezos et al. utilizing ultrasound chemo-immunotherapy responses mouse tumors. Through training optimization large number this highlights combining it important understand all respond same way therapy. While benefit not, different how will chemotherapy immunotherapy. Specifically, we looked at difficult-to-treat structures. These softened certain drugs making them more responsive computer method analyze measure Our trained set able predict well would Overall, could improve using non-invasive therapies most effective each patient.

Language: Английский

Citations

5
A Hybrid Deep Learning and Machine Learning Approach with Mobile-EfficientNet and Grey Wolf Optimizer for Lung and Colon Cancer Histopathology Classification DOI Open Access
Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, J A García-Rodríguez

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(22), P. 3791 - 3791

Published: Nov. 11, 2024

Lung and colon cancers are among the most prevalent lethal malignancies worldwide, underscoring urgent need for advanced diagnostic methodologies. This study aims to develop a hybrid deep learning machine framework classification of Colon Adenocarcinoma, Benign Tissue, Squamous Cell Carcinoma from histopathological images.

Language: Английский

Citations

5