Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 149, P. 102779 - 102779

Published: Jan. 24, 2024

Language: Английский

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Blood cancer prediction model based on deep learning technique

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 13, 2025

Abstract Blood cancer is among the critical health concerns people around world and normally emanates from genetic environmental issues. Early detection becomes essential, as rate of death associated with it high, to ensure that treatment success up, mortality reduced. This paper focuses on improving blood diagnosis using advanced deep learning techniques like ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, VGG19. Among models assessed, ResNetRS50 had better accuracy speed minimal error rates compared other state-of-the-arts. work will exploit power in contributing early reducing bad outcomes for patients. currently one deadliest diseases worldwide, resulting a combination non-genetic factors. It stands leading cause cancer-related deaths both developed developing nations. pivotal rates, increases likelihood successful potential cure. The objective decrease through cancer, thus offering individuals chance survival this disease.

Language: Английский

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A Comprehensive Review of Artificial Intelligence Approaches in Omics Data Processing: Evaluating Progress and Challenges

International Journal of Mathematics Statistics and Computer Science, Journal Year: 2023, Volume and Issue: 2, P. 114 - 167

Published: Dec. 25, 2023

The primary objective of this study is to review and assess the best available research on omics-related artificial intelligence (AI) methods. Furthermore, it seeks demonstrate promise AI approaches in omics data analysis identify critical problems that must be solved achieve potential fully. There are many moving parts when trying make sense a plethora through literature review. Essential components include, for instance, clinical applications collections. Other researchers have faced challenges, existing highlights them. Using systematic strategy, we searched all relevant articles utilizing multiple keyword variations. We also seek additional research, such as guidelines, studies comparison, studies. Challenges with AI, preprocessing, datasets, validation models, testbed arose was used analyze data. To solve these problems, several pertinent investigations were carried out. Our work offers unique insights into intersection model fields, setting apart from prior articles. anticipate practitioners seeking an all-encompassing perspective using processing would find study's findings invaluable.

Language: Английский

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Overview of Big Data Analytics in Modern Astronomy

International Journal of Mathematics Statistics and Computer Science, Journal Year: 2023, Volume and Issue: 2, P. 96 - 113

Published: Dec. 9, 2023

Astronomers are increasingly compelled to chart the universe with ever greater precision. Projects like Sloan Digital Sky Survey (SDSS), Pan-STARRS, and Large Synoptic Telescope (LSST) generate approximately 100-200 Petabytes of data annually, presenting significant big challenges in terms storage, processing, transfer. The Square Kilometer Array (SKA), an ambitious project involving 130,000 antennas 200 dishes spanning two continents, is scheduled become operational 2028. It will collect 160 terabytes per second, translating 1 petabyte daily. Coping this immense volume necessitates real-time processing analysis, driving need for efficient machine learning image analysis algorithms. Astronomy stands as ideal domain analytics, pushing boundaries analysis. This review paper present intriguing applications scientists, exploring recent technological advancements analytics concerning astronomy. also critically assess strengths weaknesses various approaches, methodologies, or tools used within context astronomy, supported by relevant case studies.

Language: Английский

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RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 150(10)

Published: Oct. 10, 2024

Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection exacerbated by the dimensionality and complexity gene expression data, which complicates classification process.

Language: Английский

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Systematic reviews of machine learning in healthcare: a literature review

Expert Review of Pharmacoeconomics & Outcomes Research, Journal Year: 2023, Volume and Issue: 24(1), P. 63 - 115

Published: Nov. 13, 2023

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

Language: Английский

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Multi-omics data integration and analysis pipeline for precision medicine: systematic review

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108254 - 108254

Published: Oct. 16, 2024

Language: Английский

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Optimising ovarian tumor classification using a novel CT sequence selection algorithm

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 23, 2024

Gynaecological cancers, especially ovarian cancer, remain a critical public health issue, particularly in regions like India, where there are challenges related to cancer awareness, variable pathology, and limited access screening facilities. These often lead the diagnosis of at advanced stages, resulting poorer outcomes for patients. The goal this study is enhance accuracy classifying tumours, with focus on distinguishing between malignant early-stage cases, by applying deep learning methods. In our approach, we utilized three pre-trained models-Xception, ResNet50V2, ResNet50V2FPN-to classify tumors using publicly available Computed Tomography (CT) scan data. To further improve model's performance, developed novel CT Sequence Selection Algorithm, which optimises use images more precise classification tumours. models were trained evaluated selected TIFF images, comparing performance ResNet50V2FPN model without Algorithm. Our experimental results show Comparative evaluation against ResNet50V2 FPN model, both demonstrates superiority proposed algorithm over existing state-of-the-art This research presents promising approach improving early detection management gynecological potential benefits patient outcomes, areas healthcare resources.

Language: Английский

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A new dimensionality reduction technique based on the Wavelet Transform for cancer classification

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

Published: Jan. 21, 2025

Language: Английский

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Advanced deep learning and large language models: Comprehensive insights for cancer detection

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105495 - 105495

Published: March 1, 2025

Language: Английский

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