MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks DOI Creative Commons
Omneya Attallah, Dina A. Ragab, Maha Sharkas

и другие.

PeerJ, Год журнала: 2020, Номер 8, С. e10086 - e10086

Опубликована: Сен. 30, 2020

Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of present era one most crucial hazards threatening worldwide health. Therefore, early detection COVID-19 essential. The common way to detect reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable suspected patients, however, overlap between patterns other types pneumonia makes difficult for radiologists diagnose accurately. On hand, deep learning (DL) techniques especially convolutional neural network (CNN) classify non-COVID-19 cases. In addition, DL that use CT images deliver an accurate diagnosis faster than RT-PCR which consequently saves time disease control provides efficient computer-aided (CAD) system. shortage publicly available datasets images, CAD system’s design a challenging task. systems literature are based on either individual CNN or two-fused CNNs; used segmentation classification diagnosis. this article, novel system proposed diagnosing fusion multiple CNNs. First, end-to-end performed. Afterward, features extracted from each individually classified using support vector machine (SVM) classifier. Next, principal component analysis applied feature set, network. Such sets then train SVM classifier individually. selected number components set fused compared with CNN. results show effective capable detecting distinguishing cases accuracy 94.7%, AUC 0.98 (98%), sensitivity 95.6%, specificity 93.7%. Moreover, efficient, as fusing reduced computational cost final model by almost 32%.

Язык: Английский

Deep learning in medical imaging and radiation therapy DOI Open Access
Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski

и другие.

Medical Physics, Год журнала: 2018, Номер 46(1)

Опубликована: Окт. 27, 2018

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved date; (b) identify common unique challenges, strategies that researchers have taken address these challenges; (c) some the promising avenues for future both terms applications as well technical innovations. We introduce general principles DL convolutional neural networks, survey five major areas application therapy, themes, discuss methods dataset expansion, conclude by summarizing lessons learned, remaining directions.

Язык: Английский

Процитировано

660
Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine DOI Open Access
Ryuji Hamamoto, Kruthi Suvarna, Masayoshi Yamada

и другие.

Cancers, Год журнала: 2020, Номер 12(12), С. 3532 - 3532

Опубликована: Ноя. 26, 2020

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI medical field. More than 60 AI-equipped already been approved by Food and Drug Administration (FDA) United States, active introduction is considered be an inevitable trend future medicine. field oncology, applications using are underway, mainly radiology, expected positioned as important core technology. particular, “precision medicine,” a treatment that selects most appropriate for each patient based on vast amount data such genome information, has become worldwide trend; utilized process extracting truly useful information from large applying it diagnosis treatment. this review, we would like introduce history current state AI, especially oncology field, well discuss possibilities challenges

Язык: Английский

Процитировано

159
A framework for breast cancer classification using Multi-DCNNs DOI
Dina A. Ragab, Omneya Attallah, Maha Sharkas

и другие.

Computers in Biology and Medicine, Год журнала: 2021, Номер 131, С. 104245 - 104245

Опубликована: Янв. 29, 2021

Язык: Английский

Процитировано

149
Future prospects of computer-aided design (CAD) – A review from the perspective of artificial intelligence (AI), extended reality, and 3D printing DOI Creative Commons

Bonsa Regassa Hunde,

Abraham Debebe Woldeyohannes

Results in Engineering, Год журнала: 2022, Номер 14, С. 100478 - 100478

Опубликована: Июнь 1, 2022

Computer-aided design (CAD) is the use of computer-based software to aid in modeling, analysis, review, and documentation. Nevertheless, benefits CAD can be elevated combination with artificial intelligence (AI), extended reality, manufacturing. AI create an intelligent graphics interface change tedious processes into sophisticated ones. In reality technology, simulation take place a 3D virtual environment, thereby providing excellent interaction better analysis. manufacturing, as seen printing systems directly connected manufacturing produce complex parts easily rapidly. this paper, integration (AI) CAD, well application examined. The primary aim review present overview current state-of-the-art its applications, forecast future prospects. article written using systematic journal papers focus on wide spectrum potentially relevant researches CAD. incorporating systems, printing, finally brief discussion issues that are pushing new levels all discussed. Finally, concluded demand for several varied products based single object input, immersive interactive simulation, direct design-to-manufacturing driving levels.

Язык: Английский

Процитировано

130
Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis DOI Creative Commons
Yawei Li, Wu Xin, Ping Yang

и другие.

Genomics Proteomics & Bioinformatics, Год журнала: 2022, Номер 20(5), С. 850 - 866

Опубликована: Окт. 1, 2022

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study lung cancer. Meanwhile, human mind is limited effectively handling fully utilizing accumulation such enormous amounts data. Machine learning-based approaches play a critical role integrating analyzing these large complex datasets, which have extensively characterized cancer through use different perspectives from accrued In this article, we provide an overview machine that strengthen varying aspects diagnosis therapy, including early detection, auxiliary diagnosis, prognosis prediction, immunotherapy practice. Moreover, highlight challenges opportunities for future applications learning

Язык: Английский

Процитировано

116
Flexible and Stretchable Light-Emitting Diodes and Photodetectors for Human-Centric Optoelectronics DOI
Sehui Chang, Ja Hoon Koo, Jisu Yoo

и другие.

Chemical Reviews, Год журнала: 2024, Номер 124(3), С. 768 - 859

Опубликована: Янв. 19, 2024

Optoelectronic devices with unconventional form factors, such as flexible and stretchable light-emitting or photoresponsive devices, are core elements for the next-generation human-centric optoelectronics. For instance, these deformable can be utilized closely fitted wearable sensors to acquire precise biosignals that subsequently uploaded cloud immediate examination diagnosis, also used vision systems human-interactive robotics. Their inception was propelled by breakthroughs in novel optoelectronic material technologies device blueprinting methodologies, endowing flexibility mechanical resilience conventional rigid devices. This paper reviews advancements soft technologies, honing on various materials, manufacturing techniques, design strategies. We will first highlight general approaches fabrication, including appropriate selection substrate, electrodes, insulation layers. then focus materials diodes, their integration strategies, representative application examples. Next, we move photodetectors, highlighting state-of-the-art fabrication methods, followed At end, a brief summary given, potential challenges further development of functional discussed conclusion.

Язык: Английский

Процитировано

67
PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images DOI

Taha Muezzinoglu,

Nursena Bayğın, Ilknur Tuncer

и другие.

Journal of Digital Imaging, Год журнала: 2023, Номер 36(3), С. 973 - 987

Опубликована: Фев. 16, 2023

Язык: Английский

Процитировано

51
Deep learning-based PET image denoising and reconstruction: a review DOI Creative Commons
Fumio Hashimoto, Yuya Onishi,

Kibo Ote

и другие.

Radiological Physics and Technology, Год журнала: 2024, Номер 17(1), С. 24 - 46

Опубликована: Фев. 6, 2024

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview conventional methods from filtered backprojection through to recent iterative algorithms, then deep learning for data up latest innovations within three main categories. The first category involves post-processing denoising. second comprises direct that learn mappings sinograms reconstructed images in end-to-end manner. third combine with neural-network enhancement. We discuss future perspectives technology.

Язык: Английский

Процитировано

28
Digital Technologies in the Sustainable Design and Development of Textiles and Clothing—A Literature Review DOI Open Access

Martina Glogar,

Slavenka Petrak, Maja Mahnić Naglić

и другие.

Sustainability, Год журнала: 2025, Номер 17(4), С. 1371 - 1371

Опубликована: Фев. 7, 2025

This paper examines the digital transformation of textile and fashion industry, focusing on alignment with sustainability principles through integration Industry 4.0 technologies. The introduction highlights urgency transitioning from conventional production methods to innovative, digitally enabled systems that promote a circular economy resource efficiency. main research questions address contribution elements sustainable solutions, directions digitalization within apparel sector, significant impact technologies achievement goals. theoretical framework in industry emphasizes need for green facilitated by reduce environmental impacts. concepts, as discussed Concept Textile Apparel Sector, are revolutionizing such IoT, AI, blockchain, enabling traceability, customization, energy-efficient operations. also explores evolution into high-tech highlighting advances CAD-CAM systems, printing, 3D improve precision, waste, support practices. In its conclusion, crucial role interdisciplinary collaboration, regulatory frameworks, investment skills development overcome challenges implementing It posits strategic embrace ecosystems is essential creating resilient aligned societal

Язык: Английский

Процитировано

6
Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review DOI Creative Commons
Jasper Jonathan Twilt, Kicky G. van Leeuwen, Henkjan Huisman

и другие.

Diagnostics, Год журнала: 2021, Номер 11(6), С. 959 - 959

Опубликована: Май 26, 2021

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude artificial intelligence (AI) applications have been suggested aid in diagnosis and detection PCa. In this review, we provide an overview current field, including studies between 2018 February 2021, describing AI algorithms (1) lesion classification (2) Our evaluation 59 included showed that most research has conducted task PCa (66%) followed by (34%). Studies large heterogeneity cohort sizes, ranging 18 499 patients (median = 162) combined with different approaches performance validation. Furthermore, 85% reported on stand-alone diagnostic accuracy, whereas 15% demonstrated impact thinking efficacy, indicating limited proof clinical utility applications. order introduce within workflow assessment, robustness generalizability need be further validated utilizing external validation experiments.

Язык: Английский

Процитировано

71