Quantum computational infusion in extreme learning machines for early multi-cancer detection

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 6, 2025

Language: Английский

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Enhancing Generalization and Mitigating Overfitting in Deep Learning for Brain Cancer Diagnosis from MRI

Applied Magnetic Resonance, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

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Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: Feb. 4, 2025

Background Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications chemotherapy development, cancer diagnosis, and predicting response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) deep (DL). Objective This review aims to explore role forecasting outcomes related treatment response, synthesizing current advancements identifying critical gaps field. Methods A comprehensive literature search was conducted across PubMed, Embase, Web Science, Cochrane databases up 2023. Keywords included “Artificial Intelligence (AI),” “Machine Learning (ML),” “Deep (DL)” combined with “chemotherapy development,” “cancer diagnosis,” treatment.” Articles published within last four years written English were included. The Prediction Model Risk Bias Assessment utilized assess risk bias selected studies. Conclusion underscores substantial impact AI, including ML DL, on innovation, response for both solid hematological tumors. Evidence from recent studies highlights AI’s potential reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing plans, improving therapeutic outcomes. Future research should focus addressing challenges clinical implementation, ethical considerations, scalability enhance integration into oncology care.

Language: Английский

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Improved Brain Tumor Segmentation Using Modified U-Net based on Particle Swarm Optimization Image Enhancement

Proceedings of the Genetic and Evolutionary Computation Conference Companion, Journal Year: 2024, Volume and Issue: unknown, P. 611 - 614

Published: July 14, 2024

Language: Английский

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Revolutionizing diagnosis of pulmonary Mycobacterium tuberculosis based on CT: a systematic review of imaging analysis through deep learning

Frontiers in Microbiology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 8, 2025

The mortality rate associated with Mycobacterium tuberculosis (MTB) has seen a significant rise in regions heavily affected by the disease over past few decades. traditional methods for diagnosing and differentiating (TB) remain thorny issues, particularly areas high TB epidemic inadequate resources. Processing numerous images can be time-consuming tedious. Therefore, there is need automatic segmentation classification technologies based on lung computed tomography (CT) scans to expedite enhance diagnosis of TB, enabling rapid secure identification condition. Deep learning (DL) offers promising solution automatically segmenting classifying CT scans, expediting enhancing diagnosis. This review evaluates diagnostic accuracy DL modalities pulmonary (PTB) after searching PubMed Web Science databases using preferred reporting items systematic reviews meta-analyses (PRISMA) guidelines. Seven articles were found included review. While been widely used achieved great success CT-based PTB diagnosis, are still challenges addressed opportunities explored, including data scarcity, model generalization, interpretability, ethical concerns. Addressing these requires augmentation, interpretable models, moral frameworks, clinical validation. Further research should focus developing robust generalizable establishing guidelines, conducting validation studies. holds promise transforming improving patient outcomes.

Language: Английский

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Feasibility Study of Detecting and Segmenting Small Brain Tumors in a Small MRI Dataset with Self-Supervised Learning

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 249 - 249

Published: Jan. 22, 2025

Objectives: This paper studies the segmentation and detection of small metastatic brain tumors. study aims to evaluate feasibility training a deep neural network for tumors in MRI using very dataset 33 cases, by leveraging large public datasets primary tumors; Methods: explores various methods, including supervised learning, two transfer learning approaches, self-supervised utilizing U-net Swin UNETR models; Results: The approach model yielded best performance. Dice score was approximately 0.19. Sensitivity reached 100%, while specificity 54.5%. When excluding subjects with hyperintensities, improved 80.0%; Conclusions: It is feasible train

Language: Английский

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Exploring brain tumor detection through artificial intelligence

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 93 - 120

Published: Jan. 1, 2025

Language: Английский

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Circulating microRNAs: A remarkable opportunity as non-invasive biomarkers from adult to pediatric brain tumor patients

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104650 - 104650

Published: Feb. 1, 2025

Language: Английский

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From Traditional Methods to 3D U-Net: A Comprehensive Review of Brain Tumour Segmentation Techniques

Journal of Biomedical Science and Engineering, Journal Year: 2025, Volume and Issue: 18(01), P. 1 - 32

Published: Jan. 1, 2025

Language: Английский

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A comprehensive review on deep learning approaches for brain tumour classification and prediction: Current status and future prospects

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3281, P. 060005 - 060005

Published: Jan. 1, 2025

Language: Английский

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