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Deep Learning with Applications Using Python

Navin Kumar Manaswi • Apress eBooks (2018)

This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such ...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-3516-4

Computer‐aided diagnosis in the era of deep learning

Heang‐Ping Chan, Lubomir M. Hadjiiski, Ravi K. Samala • Medical Physics (2020)

Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then ...

OPENALEX Open Access DOI: https://doi.org/10.1002/mp.13764 PMC: 7293164

Face recognition in dense crowd using deep learning approaches with IP camera

Sobhana Mummaneni, Venkata Chaitanya Satya Ramaraju Mudunuri, Sri Veerabhadra Vikas Bommaganti, Bhavya Vani Kalle et al. • Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (2025)

A facial recognition system is a biometric security and surveillance system that can identify and monitor individuals in a crowded area. Manually monitoring a crowded environment is a difficult and error-prone task. Therefore, in such conte...

DOAJ Open Access (DOAJ) DOI: 10.35784/iapgos.6818

Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI

Syarifah Muliana, Taufiq Taufiq, Munirul Ula • Journal of Applied Informatics and Computing (2026)

Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for ...

DOAJ Open Access (DOAJ) DOI: 10.30871/jaic.v10i3.12864

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction

Naledi Lenah Adam, Grzegorz Kowalik, Andrew Tyler, Ronald Mooiweer et al. • Frontiers in Cardiovascular Medicine (2024)

BackgroundSimultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image recon...

DOAJ Open Access (DOAJ) DOI: 10.3389/fcvm.2024.1350345

Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

A. I. M. Schwebig, A. I. M. Schwebig, R. Tutsch • Journal of Sensors and Sensor Systems (2020)

Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particu...

DOAJ Open Access (DOAJ) DOI: 10.5194/jsss-9-167-2020

HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation

Iraj Abedi, Mahsa Vali, Bentolhoda Otroshi, Maryam Zamanian et al. • BMC Research Notes (2024)

Abstract Introduction Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the ...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13104-024-06693-z

Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video)

Jiao Li, Xiaojuan Jing, Qin Zhang, Xiaoxiang Wang et al. • BMC Medical Informatics and Decision Making (2025)

Abstract Background Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tu...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-025-03147-9

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Hongfan Liao, Jiang Yuan, Chunhua Liu, Jiao Zhang et al. • Insights into Imaging (2023)

Abstract Objective This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC)...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13244-023-01553-z

Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau

Junpo Yu, Yajun Si, Wen Zhao, Zeyu Zhou et al. • Plants (2025)

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and...

DOAJ Open Access (DOAJ) DOI: 10.3390/plants14152391

Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning

Synho Do, Kyoung Doo Song, Joo Won Chung • Korean Journal of Radiology (2019)

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on...

OPENALEX cc-by-nc DOI: https://doi.org/10.3348/kjr.2019.0312 PMC: 6960318

A review of deep learning in image recognition

Myeongsuk Pak, Sang-Hoon Kim • International Conference on Computer Applications and Information Processing Technology (CAIPT) (2017)

Deep learning has been the core topic in machine learning and convolutional neural network is one of the most prominent approaches. Convolutional neural network has won numerous competitions in recent years. It has outstanding results in im...

OPENALEX DOI: https://doi.org/10.1109/caipt.2017.8320684

Deep Learning for Spatio-Temporal Data Mining: A Survey

Senzhang Wang, Jiannong Cao, Philip S. Yu • IEEE Transactions on Knowledge and Data Engineering (2020)

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temp...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2020.3025580

Deep Learning and Its Application to LHC Physics

D. Guest, Kyle Stuart Cranmer, Daniel Whiteson • Annual Review of Nuclear and Particle Science (2018)

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex ...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-nucl-101917-021019