Clustering performance using k-modes with modified entropy measure for breast cancer DOI Open Access
Nurshazwani Muhamad Mahfuz, Heru Suhartanto,

Kusmardi Kusmardi

et al.

Indonesian Journal of Electrical Engineering and Computer Science, Journal Year: 2023, Volume and Issue: 32(2), P. 1150 - 1150

Published: Sept. 24, 2023

<span>Breast cancer is a serious disease that requires data analysis for diagnosis and treatment. Clustering mining technique often used in breast research to assess the level of malignancy at an early stage. However, clustering categorical can be challenging because different levels variables impact process. This proposes modified entropy measure (MEM) enhance performance. MEM aims address issue distance-based measures data. It also useful tool assessing loss clustering, which helps understand patterns relationships by quantifying information lost during clustering. An evaluation compares k-modes+MEM, k-means+MEM, DBSCAN+MEM, affinity+MEM with conventional algorithms. The assessment metrics accuracy, intra-cluster distance fowlkes-mallow index (FMI) are employed evaluate algorithm Experimental results show significant improvements. k-Modes+MEM achieves reduction average outperforms other algorithms distance, FMI. proposed extended heterogeneous datasets various domains such as healthcare, finance, marketing.</span>

Language: Английский

Recent advances of liquid biopsy: Interdisciplinary strategies toward clinical decision‐making DOI Creative Commons
Han Bao, Li Min,

Fanqin Bu

et al.

Deleted Journal, Journal Year: 2023, Volume and Issue: 1(4)

Published: Oct. 1, 2023

Abstract Liquid biopsy has emerged as a promising avenue for non‐invasive and rapid retrieval of pathological information from patient body fluids. Over the years, liquid garnered significant attention clinically treating cancer by selecting appropriate biomarkers such circulating tumor cells (CTCs) extracellular vesicles (EVs). Further integration advanced technologies facilitated efficient capture biopsy, revolutionizing clinical decision‐making in multiple processes stages patients. Underscoring intersection different disciplines, this review provides holistic summary recent breakthroughs specifically designed application distinctive to blend real‐world with material science. Firstly, we focus on main principles that facilitate release (e.g., CTCs EVs), leveraging their physicochemical properties. Then, applications are summarized, highlighting potential providing comprehensive information. Later, incorporation machine learning is also emphasized enhancing enabling deeper insights design next‐generation platforms specific biomarker isolation. Finally, future opportunities explored combining nanotechnologies artificial intelligence, thereby offering inconceivable possibilities improving care.

Language: Английский

Citations

48
A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation DOI

Teng Wang,

Jun Liu, Jinshan Tang

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

Breast cancer remains a significant global health concern and is leading cause of mortality among women. The accuracy breast diagnosis can be greatly improved with the assistance automatic segmentation ultrasound images. Research has demonstrated effectiveness convolutional neural networks (CNNs) transformers in segmenting these Some studies combine CNNs, using transformer's ability to exploit long-distance dependencies address limitations inherent networks. Many face due forced integration transformer blocks into CNN architectures. This approach often leads inconsistencies feature extraction process, ultimately resulting suboptimal performance for complex task medical image segmentation. paper presents CSAU-Net, cross-scale attention-guided U-Net, which combined CNN-transformer structure that leverages local detail depiction CNNs handle dependencies. To integrate context data, we propose cross-attention block embedded within skip connections U-shaped architectural network. further enhance incorporated gated dilated convolution (GDC) module lightweight channel self-attention (LCAT) on encoder side. Extensive experiments conducted three open-source datasets demonstrate our CSAU-Net surpasses state-of-the-art techniques lesions.

Language: Английский

Citations

3
A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images DOI Creative Commons

Salman Zakareya,

Habib Izadkhah, Jaber Karimpour

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(11), P. 1944 - 1944

Published: June 1, 2023

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection disease can be lifesaving. Detecting breast allows for treatment to begin faster, increasing chances a successful outcome. Machine learning helps in even places where there no access specialist doctor. The rapid advancement machine learning, particularly deep leads an increase medical imaging community's interest applying these techniques improve accuracy screening. Most data related diseases scarce. On other hand, deep-learning models need much learn well. For this reason, existing on images cannot work as well images. To overcome limitation classification detection, inspired by two state-of-the-art networks, GoogLeNet residual block, developing several new features, paper proposes model classify cancer. Utilizing adopted granular computing, shortcut connection, learnable activation functions instead traditional functions, attention mechanism expected diagnosis consequently decrease load doctors. Granular computing capturing more detailed fine-grained information about proposed model's superiority demonstrated comparing it works using case studies. achieved 93% 95% ultrasound histopathology images, respectively.

Language: Английский

Citations

26
Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence DOI Open Access
Mariia Ivanova, Carlo Pescia, Dario Trapani

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(11), P. 1981 - 1981

Published: May 23, 2024

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining categories remains challenging. This paper explores evolving approaches stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep convolutional neural networks, AI reshaping predictive algorithms recurrence risk, thereby revolutionizing diagnostic accuracy treatment planning. Beyond detection, applications extend to histological subtyping, grading, lymph node assessment, feature identification, fostering personalized therapy decisions. With rising rates, it crucial implement accelerate breakthroughs practice, benefiting both patients healthcare providers. However, important recognize that while offers powerful automation analysis tools, lacks the nuanced understanding, context, ethical considerations inherent human pathologists patient care. Hence, successful integration of into practice demands collaborative efforts between medical experts computational optimize outcomes.

Language: Английский

Citations

5
Medical sensor network and machine learning-enabled digital twins for diagnostic and therapeutic purposes DOI
Anna Paleczek, Artur Rydosz

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 77 - 94

Published: Jan. 1, 2025

Language: Английский

Citations

0
Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review DOI Creative Commons
Hein Minn Tun, Hanif Abdul Rahman, Lin Naing

et al.

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 15, 2025

Cancer remains a significant health challenge in the ASEAN region, highlighting need for effective screening programs. However, approaches, target demographics, and intervals vary across member states, necessitating comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as tool cancer screening, its utilization region is unexplored. This study aims identify evaluate different programs ASEAN, with focus on assessing integration impact AI A scoping review was conducted using PRISMA-ScR guidelines provide overview usage ASEAN. Data were collected from government ministries, official guidelines, literature databases, relevant documents. The use reviews involved searches through PubMed, Scopus, Google Scholar inclusion criteria only included studies that utilized data January 2019 May 2024. findings reveal diverse approaches Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia Timor-Leste primarily adopt opportunistic Singapore, Malaysia, Thailand organized Cervical widespread, both methods. Fourteen review, covering breast (5 studies), cervical (2 colon (4 hepatic (1 study), lung oral study) cancers. Studies revealed stages screening: prospective clinical evaluation (50%), silent trial (36%) exploratory model development (14%), promising results enhancing accuracy efficiency. require more targeting appropriate age groups at regular meet WHO's 2030 targets. Efforts integrate Thailand, show optimizing processes, reducing costs, improving early detection. technology enhances identification during detection management region.

Language: Английский

Citations

0
An explainable AI for breast cancer classification using vision Transformer (ViT) DOI

Marwa Naas,

Hiba Mzoughi, Ines Njeh

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 108, P. 108011 - 108011

Published: May 2, 2025

Language: Английский

Citations

0
Mapping dominant plant communities in the degraded Zoige swamp using Sentinel-1/2 imagery and its implications for vegetation restoration DOI
Guoying Zhang, Chuanpeng Zhao,

Mingming Jia

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 175, P. 113557 - 113557

Published: May 9, 2025

Language: Английский

Citations

0
Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis DOI Creative Commons
Giovanni Pietro Burrai, Andrea Gabrieli, Marta Polinas

et al.

Animals, Journal Year: 2023, Volume and Issue: 13(9), P. 1563 - 1563

Published: May 6, 2023

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, ability of CAD systems to distinguish benign from malignant CMTs has been explored on dataset-namely CMTD-of 1056 hematoxylin eosin JPEG images 20 24 CMTs, with three different based combination convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as feature extractor, classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed top net. Based human breast cancer dataset (i.e., BreakHis) (accuracy 0.86 0.91), our models were applied CMT dataset, showing accuracy 0.63 0.85 across all architectures. The EfficientNet framework coupled SVM resulted best performances an 0.82 0.85. encouraging results obtained use DP provide interesting perspective integration artificial intelligence machine learning technologies cancer-related research.

Language: Английский

Citations

7
Towards Agility in Breast Cancer Treatment Principles as Adopted from Agile Software Engineering DOI Creative Commons
Yousra Odeh, Mahmoud Al-Balas

Journal of Multidisciplinary Healthcare, Journal Year: 2024, Volume and Issue: Volume 17, P. 1315 - 1341

Published: March 1, 2024

Purpose: The complex nature of breast cancer demands flexible and adaptable principles that can account for the diverse characteristics evolving conditions each patient. However, there are no common treatment agility influence policies direct professionals healthcare providers into enhancing delivery health outcomes to patients under these along with continuous rapid improvements in plan design. incorporation agile from software engineering offers a promising avenue patient care. This research is conducted identify adopted field validate their conformance through work reported literature context. Material Methods: authors applied structured methodology involved interviews eliciting validating twelve oncologists. Discussion principle reflected using as form validation. Finally, domain expert reviewed literature-driven validation identified finally provide results. Results: resulted validated classified whether they meeting, partially-(hybrid), or not meeting agility. Seven out agility, where remaining five partially None them recorded Conclusion: contributes forming an mindset empower optimize plans, enhance experiences, continuously improve quality anticipated contribute driving more efficient oncology practices, policies, protocols. It concluded limited twelve. Keywords: cancer, treatment, agile, oncology, engineering, healthcare, principles, multidisciplinary research, policy

Language: Английский

Citations

2