Log in

Relevant bibliographies by topics / Deep Learning in Healthcare / Journal articles

To see the other types of publications on this topic, follow the link: Deep Learning in Healthcare.

Journal articles on the topic 'Deep Learning in Healthcare'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Deep Learning in Healthcare.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Hu, Shengkun. "Deep Learning in Healthcare." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 279–85. http://dx.doi.org/10.54097/hset.v57i.10014.

Full text

Abstract:

This article aims to discuss and demonstrate deep learning techniques used in healthcare. After introducing the feasibility of deep learning in the medical field, the article discussed the opportunities and challenges of deep learning in healthcare from different perspectives. Then, the article showed the current implementations and applications of deep learning in the medical healthcare system. Finally, the article summarizes deep learning techniques and applications in healthcare.

APA, Harvard, Vancouver, ISO, and other styles

2

B, Thulasi Thanmai, Vani K., Dwaraka Srihith I., Venkat Sai I., and Shasikala I. "Revolutionizing Healthcare with Deep Learning." Recent Trends in Information Technology and its Application 6, no. 3 (2023): 16–30. https://doi.org/10.5281/zenodo.8138446.

Full text

Abstract:

<em>Deep learning technology is becoming increasingly prevalent in the healthcare industry, which has the potential to revolutionise medical diagnosis, treatment, and patient care. Deep learning algorithms are capable of analysing immense quantities of healthcare data, such as patient records, medical images, and genomic information, to identify patterns and make highly accurate predictions. This technology is currently being used, among other things, to enhance diagnostic accuracy, personalise treatment plans, and predict patient outcomes.</em>

APA, Harvard, Vancouver, ISO, and other styles

3

Tyagi, Himanshu, Aditya Gupta, Saurabh Srivastava, Neeraj Kumari, Aryan Sharma, and Nikhil Sharma. "AI in Healthcare: Deep Learning Solutions for Lung Cancer Detection." International Journal of Research Publication and Reviews 6, sp5 (2025): 402–6. https://doi.org/10.55248/gengpi.6.sp525.1957.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Pant, Sakshi. "Deep Learning for Personalized Healthcare Recommendations." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 470–75. http://dx.doi.org/10.22214/ijraset.2024.65093.

Full text

Abstract:

Personalized healthcare refers to an evolving paradigm of providing appropriate medical treatments based on the particularities of the individual patient, where evidence-based management is enhanced with the use of technologies. Deep learning (DL) is placed within the umbrella of efficient systems known as artificial intelligence (AI), it assists in performing data processing with more accuracy, and making suggestions based on the unique health information of the health record e.g. EHRs, images and genetic data among others. This article gives an overview of deep learning techniques in develop

APA, Harvard, Vancouver, ISO, and other styles

5

Norgeot, Beau, Benjamin S. Glicksberg, and Atul J. Butte. "A call for deep-learning healthcare." Nature Medicine 25, no. 1 (2019): 14–15. http://dx.doi.org/10.1038/s41591-018-0320-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Ganesh Viswanathan, Gaurav Samdani, Yawal Dixit, and Ranjith Gopalan. "Deep Learning." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 512–27. https://doi.org/10.30574/wjaets.2025.14.3.0149.

Full text

Abstract:

Deep learning has revolutionized artificial intelligence by enabling machines to learn complex patterns from vast amounts of data. This white paper explores the fundamental principles of deep learning, including neural network architectures, training methodologies, and key advancements such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. We discuss applications across various domains, including computer vision, natural language processing, healthcare, and finance, highlighting real-world use cases and breakthroughs. Additionally, we examine th

APA, Harvard, Vancouver, ISO, and other styles

7

Mrs., P. Menaka, Subhashita J., Parthiban S., Sooraj S., and Dinesh R. "Medical Imaging Using Deep Learning." International Journal of Engineering and Management Research 14, no. 1 (2024): 40–43. https://doi.org/10.5281/zenodo.10646035.

Full text

Abstract:

The healthcare sector has been transformed by deep learning, a kind of artificial intelligence Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two examples of deep learning techniques that have been used to evaluate medical pictures, forecast illness outcomes, and enhance patient care. This study examines the important strides made by deep learning in the fields of radiology, pathology, genomics, and electronic health records (EHRs). Additionally, it draws attention to the difficulties and moral issues that come with the application of deep learning in healthcare,

APA, Harvard, Vancouver, ISO, and other styles

8

Anjum, Uzma. "Artificial Intelligence, Machine Learning and Deep Learning In Healthcare." Bioscience Biotechnology Research Communications 14, no. 7 (2021): 144–48. http://dx.doi.org/10.21786/bbrc/14.7.36.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Saxena, Ms Kavita, Rishabh Sharma, Rishav Kumar, and Roshan Kumar. "Disease Prediction Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2655–63. http://dx.doi.org/10.22214/ijraset.2022.42871.

Full text

Abstract:

Abstract: Health being the state of complete physical and mental wellbeing is an imperative part of humankind .Healthcare sector been a capital incentive sector having complicated entry barrier for investors like acquiring land for making hospital, stamp duties on it, human resource crunch which further act as roadblock for the government in providing universal good healthcare services to its citizenry . In this regard artificial intelligence is leading to disruption in the healthcare sector which is helping poor in safeguarding them from been exploited by extravagant out of pocket expenditure

APA, Harvard, Vancouver, ISO, and other styles

10

Vinothkumar, Kolluru. "Healthcare Through AI: Integrating Deep Learning, Federated Learning, and XAI for Disease Management." International Journal of Soft Computing and Engineering (IJSCE) 13, no. 6 (2024): 21–27. https://doi.org/10.35940/ijsce.D3646.13060124.

Full text

Abstract:

<strong>Abstract:</strong> The applications of Artificial Intelligence (AI) have been resonating across various fields for the past three decades, with the healthcare domain being a primary beneficiary of these innovations and advancements. Recently, AI techniques such as deep learning, machine learning, and federated learning have been frequently employed to address challenges in disease management. However, these techniques often face issues related to transparency, interpretability, and explainability. This is where explainable AI (XAI) plays a crucial role in ensuring the explainability of

APA, Harvard, Vancouver, ISO, and other styles

11

Baji, Adam. "Enhancing Healthcare Predictions with Deep Learning Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23729–30. http://dx.doi.org/10.1609/aaai.v38i21.30543.

Full text

Abstract:

This study leverages Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance diagnostics and predictions in healthcare. By training on extensive healthcare datasets, this project aims to improve early disease detection and health risk assessments. Evaluation emphasizes accuracy, reliability, and ethical considerations, including bias mitigation. This research promises to bridge AI advancements and clinical applications, offering significant improvements in diagnostic capabilities and healthcare accessibility.

APA, Harvard, Vancouver, ISO, and other styles

12

Rana, Ajay, and Mamta Bansal. "An overview of deep learning in healthcare." Asian Journal of Multidimensional Research 10, no. 10 (2021): 288–94. http://dx.doi.org/10.5958/2278-4853.2021.00924.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Suo, Qiuling, Fenglong Ma, Ye Yuan, et al. "Deep Patient Similarity Learning for Personalized Healthcare." IEEE Transactions on NanoBioscience 17, no. 3 (2018): 219–27. http://dx.doi.org/10.1109/tnb.2018.2837622.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Bian, Jiang, Jae S. Min, Mattia Prosperi, and Mo Wang. "Re: A Call for Deep-learning Healthcare." Epidemiology 31, no. 2 (2020): e22. http://dx.doi.org/10.1097/ede.0000000000001155.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Dai, Yinglong, and Guojun Wang. "A deep inference learning framework for healthcare." Pattern Recognition Letters 139 (November 2020): 17–25. http://dx.doi.org/10.1016/j.patrec.2018.02.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Esteva, Andre, Alexandre Robicquet, Bharath Ramsundar, et al. "A guide to deep learning in healthcare." Nature Medicine 25, no. 1 (2019): 24–29. http://dx.doi.org/10.1038/s41591-018-0316-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Deng, Xiaoyi, and Feifei Huangfu. "Collaborative Variational Deep Learning for Healthcare Recommendation." IEEE Access 7 (2019): 55679–88. http://dx.doi.org/10.1109/access.2019.2913468.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Yuan, Weiwei, Chenliang Li, Donghai Guan, Guangjie Han, and Asad Masood Khattak. "Socialized healthcare service recommendation using deep learning." Neural Computing and Applications 30, no. 7 (2018): 2071–82. http://dx.doi.org/10.1007/s00521-018-3394-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Yang, Hsuan-Chia, Md Mohaimenul Islam, and Yu-Chuan (Jack) Li. "Potentiality of deep learning application in healthcare." Computer Methods and Programs in Biomedicine 161 (July 2018): A1. http://dx.doi.org/10.1016/j.cmpb.2018.05.014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Sharma, Khushi, Amrinder Kaur, Shaveta Bhatia, Nripendra Narayan Das, and Madhulika. "Predicting Emergency Healthcare Requirements Using Deep Learning." Revue d'Intelligence Artificielle 38, no. 2 (2024): 701–6. http://dx.doi.org/10.18280/ria.380234.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Et. al., A. Lakshmi,. "A Review on Deep Learning Algorithms in Healthcare." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 5682–86. http://dx.doi.org/10.17762/turcomat.v12i10.5379.

Full text

Abstract:

Deep Learning is a branch of Machine Learning (ML) that is used to solve complex problems and come up with intelligent solutions. Artificial Neural Networks (ANN) are used in deep learning to analyze data and make predictions. In the health sector, deep learning has made it possible to use computer aided technology to predict and diagnose the disease. In this paper , we present a comprehensive review on deep learning algorithms and how it is used in healthcare to predict and diagnose various diseases.

APA, Harvard, Vancouver, ISO, and other styles

22

Abdulwahhab, Ali H., Noof T. Mahmood, Ali Abdulwahhab Mohammed, Indrit Myderrizi, and Mustafa Hamid Al-Jumaili. "A Review on Medical Image Applications Based on Deep Learning Techniques." Journal of Image and Graphics 12, no. 3 (2024): 215–27. http://dx.doi.org/10.18178/joig.12.3.215-227.

Full text

Abstract:

The integration of deep learning in medical image analysis is a transformative leap in healthcare, impacting diagnosis and treatment significantly. This scholarly review explores deep learning’s applications, revealing limitations in traditional methods while showcasing its potential. It delves into tasks like segmentation, classification, and enhancement, highlighting the pivotal roles of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Specific applications, like brain tumor segmentation and COVID-19 diagnosis, are deeply analyzed using datasets like NIH Clini

APA, Harvard, Vancouver, ISO, and other styles

23

Kumar, Ashish, and Jagdeep Kaur. "Machine Learning and Deep Learning Based Healthcare System: A Review." Clinical Case Reports and Studies 5, no. 6 (2024): 1–5. http://dx.doi.org/10.59657/2837-2565.brs.24.130.

Full text

Abstract:

The way medical data is evaluated, diagnoses are made, and patient care is provided has all changed dramatically as a result of the integration of machine learning (ML) and deep learning (DL) techniques in healthcare systems. An overview of the developments and uses of ML and DL in healthcare is provided in this study, with a focus on how they might enhance productivity, accuracy, and individualized care. The healthcare sector produces enormous volumes of data, which include patient-generated data, genetic information, electronic health records (EHRs), and medical photographs.ML algorithms hav

APA, Harvard, Vancouver, ISO, and other styles

24

Ahmad, Ahsan, Aftab Tariq, Hafiz Khawar Hussain, and Ahmad Yousaf Gill. "Revolutionizing Healthcare: How Deep Learning is poised to Change the Landscape of Medical Diagnosis and Treatment." Journal of Computer Networks, Architecture and High Performance Computing 5, no. 2 (2023): 458–71. http://dx.doi.org/10.47709/cnahpc.v5i2.2350.

Full text

Abstract:

Deep learning has become a significant tool in the healthcare industry with the potential to change the way care is provided and enhance patient outcomes. With a focus on personalised medicine, ethical issues and problems, future directions and opportunities, real-world case studies, and data privacy and security, this review article investigates the existing and potential applications of deep learning in healthcare. Deep learning in personalised medicine holds enormous promise for improving patient care by enabling more precise diagnoses and individualised treatment approaches. But it's impor

APA, Harvard, Vancouver, ISO, and other styles

25

Ali, Aitizaz, Hashim Ali, Aamir Saeed, et al. "Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning." Sensors 23, no. 18 (2023): 7740. http://dx.doi.org/10.3390/s23187740.

Full text

Abstract:

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating t

APA, Harvard, Vancouver, ISO, and other styles

26

Akhi, Sharmin Sultana, Sadia Akter, Md Refat Hossain, Arjina Akter, Nur Nobe, and Md Monir Hosen. "Early-Stage Chronic Disease Prediction Using Deep Learning: A Comparative Study of LSTM and Traditional Machine Learning Models." Frontline Medical Sciences and Pharmaceutical Journal 5, no. 07 (2025): 8–17. https://doi.org/10.37547/medical-fmspj-05-07-02.

Full text

Abstract:

Early-stage chronic disease prediction is a critical aspect of healthcare that allows for timely interventions and personalized treatment, ultimately improving patient outcomes. In this study, we explore the use of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, to predict the early stages of chronic diseases such as diabetes, cardiovascular diseases, and respiratory conditions. We compare the performance of LSTM with traditional machine learning models, including Random Forest, Gradient Boosting Machines (GBM), and Logistic Regression. The results show that LSTM

APA, Harvard, Vancouver, ISO, and other styles

27

Anas, Mohammad. "Deep Learning-based Disease Predictive Model for Healthcare." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46231.

Full text

Abstract:

Abstract. Recent advancements in healthcare underscore the critical need for innovative strategies to enhance patient outcomes. Traditional health information systems often falter when managing the immense and intricate volumes of data generated in medical settings. Deep Learning technologies provide an innovative approach, enabling the extraction of valuable insights from intricate datasets while requiring minimal human effort. Neural Health emerges as a trailblazing approach, seamlessly integrating deep learning into health information systems. Neural Health collects data from various source

APA, Harvard, Vancouver, ISO, and other styles

28

Shahzad, Yasir, Huma Javed, Haleem Farman, Jamil Ahmad, Bilal Jan, and Abdelmohsen A. Nassani. "Optimized Predictive Framework for Healthcare Through Deep Learning." Computers, Materials & Continua 67, no. 2 (2021): 2463–80. http://dx.doi.org/10.32604/cmc.2021.014904.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Nisha Gupta. "Deep Learning Applications in Healthcare: Diagnosing Diabetic Retinopathy." International Journal for Research Publication and Seminar 14, no. 5 (2024): 278–83. http://dx.doi.org/10.36676/jrps.v14.i5.1558.

Full text

Abstract:

This paper presents a deep learning model using convolutional neural networks (CNN) for the diagnosis of diabetic retinopathy. The study compares the performance of CNNs with traditional image processing techniques.

APA, Harvard, Vancouver, ISO, and other styles

30

Riaz, Hamza, Jisu Park, Peter H. Kim, and Jungsuk Kim. "Retinal Healthcare Diagnosis Approaches with Deep Learning Techniques." Journal of Medical Imaging and Health Informatics 11, no. 3 (2021): 846–55. http://dx.doi.org/10.1166/jmihi.2021.3309.

Full text

Abstract:

The retina is an important organ of the human body, with a crucial function in the vision mechanism. A minor disturbance in the retina can cause various abnormalities in the eye, as well as complex retinal diseases such as diabetic retinopathy. To diagnose such diseases in early stages, many researchers are incorporating machine learning (ML) technique. The combination of medical science with ML improves the healthcare diagnosis systems of hospitals, clinics, and other providers. Recently, AI-based healthcare diagnosis systems assist clinicians in handling more patients in less time and improv

APA, Harvard, Vancouver, ISO, and other styles

31

Miotto, Riccardo, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T. Dudley. "Deep learning for healthcare: review, opportunities and challenges." Briefings in Bioinformatics 19, no. 6 (2017): 1236–46. http://dx.doi.org/10.1093/bib/bbx044.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Purushotham, Sanjay, Chuizheng Meng, Zhengping Che, and Yan Liu. "Benchmarking deep learning models on large healthcare datasets." Journal of Biomedical Informatics 83 (July 2018): 112–34. http://dx.doi.org/10.1016/j.jbi.2018.04.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Ahmed, Shuaib, Arifa Bhutto, and Farhan Bashir. "Deep Learning Applications and Challenges for Healthcare System: A Review." International Journal of Artificial Intelligence & Mathematical Sciences 1, no. 2 (2023): 1–6. http://dx.doi.org/10.58921/ijaims.v1i2.34.

Full text

Abstract:

Deep Learning is recent biz word in artificial intelligent as well as it is the third wave of artificial intelligence research. World is rapidly growing toward the automation almost every sector is going automated their services, products and industry through AI. The recent research is indicating that deep learning application and challenges for healthcare system is big challenge although so many improvement and advancement in healthcare system in the past few decades. In the recent covid_19 pandemic has indicated so many loop holes in the healthcare automation system worldwide. Our discussion

APA, Harvard, Vancouver, ISO, and other styles

34

Gupta, Adhyayan. "Machine Learning and Deep Learning: A Comprehensive Overview." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1620–26. https://doi.org/10.22214/ijraset.2025.72470.

Full text

Abstract:

Machine Learning (ML) and Deep Learning (DL) are two core areas of Artificial Intelligence (AI) that have significantly transformed technology and research. Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, te

APA, Harvard, Vancouver, ISO, and other styles

35

Bharadhwaj, G., and G. Shivaji. "Pneumonia Detection Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44461.

Full text

Abstract:

Pneumonia is a major global health challenge requiring timely and accurate diagnosis to enhance patient outcomes. This study introduces a Convolutional Neural Network (CNN)-based model trained on labeled chest X-ray datasets to classify images as either pneumonia-positive or normal. By employing advanced techniques such as transfer learning and fine-tuning of pre-trained models, the system achieves robust feature extraction, ensuring exceptional diagnostic accuracy. Preprocessing steps, including image normalization, data augmentation, and contrast enhancement, are applied to improve the model

APA, Harvard, Vancouver, ISO, and other styles

36

Deepa Nikam. "A Review on Machine Learning and Deep Learning Approaches for Predictive Healthcare Analytics." Journal of Electrical Systems 20, no. 11s (2024): 2072–80. https://doi.org/10.52783/jes.7697.

Full text

Abstract:

This paper presents a comprehensive review on the applications of machine learning and deep learning models for various healthcare applications. As the healthcare sector increasingly shifts toward digital records and data-driven decision-making, the need for advanced computational techniques to analyze and interpret complex medical data has grown. Machine learning and deep learning have demonstrated their ability to extract meaningful insights from vast and diverse datasets, ranging from clinical records to medical imaging. This essay explores the rise of machine learning and deep learning in

APA, Harvard, Vancouver, ISO, and other styles

37

C M, Chandrashekar, and Anurag Shrivastava. "COMPETITIVE ENSEMBLE LEARNING FOR HEALTHCARE DATA ANALYSIS." ShodhKosh: Journal of Visual and Performing Arts 5, no. 6 (2024): 369–82. http://dx.doi.org/10.29121/shodhkosh.v5.i6.2024.1744.

Full text

Abstract:

In the rapidly evolving field of healthcare, the effective analysis of structured and unstructured data is crucial for enhancing disease diagnosis, patient outcomes, and overall healthcare management. This paper presents a novel approach, Competitive Ensemble Deep Learning (CEDL), designed to optimize healthcare data analysis by leveraging multiple deep learning models. Unlike traditional ensemble methods that combine weak and strong models, CEDL selectively integrates only the most effective models based on their performance, thereby improving classification accuracy and efficiency. The propo

APA, Harvard, Vancouver, ISO, and other styles

38

Yousra, Dahdouh, Anouar Boudhir Abdelhakim, and Ben Ahmed Mohamed. "A New Approach using Deep Learning and Reinforcement Learning in HealthCare." International journal of electrical and computer engineering systems 14, no. 5 (2023): 557–64. http://dx.doi.org/10.32985/ijeces.14.5.7.

Full text

Abstract:

Nowadays, skin cancer is one of the most important problems faced by the world, due especially to the rapid development of skin cells and excessive exposure to UV rays. Therefore, early detection at an early stage employing advanced automated systems based on AI algorithms plays a major job in order to effectively identifying and detecting the disease, reducing patient health and financial burdens, and stopping its spread in the skin. In this context, several early skin cancer detection approaches and models have been presented throughout the last few decades to improve the rate of skin cancer

APA, Harvard, Vancouver, ISO, and other styles

39

Oise, Godfrey Perfectson, Chioma Julia Onwuzo, Mary Fole, et al. "DECENTRALIZED DEEP LEARNING IN HEALTHCARE: ADDRESSING DATA PRIVACY WITH FEDERATED LEARNING." FUDMA JOURNAL OF SCIENCES 9, no. 6 (2025): 19–26. https://doi.org/10.33003/fjs-2025-0906-3714.

Full text

Abstract:

This study presents a privacy-preserving federated learning framework combining recurrent neural networks for healthcare applications, balancing data privacy with clinical utility. The decentralized system enables multi-institutional collaboration without centralized data collection, complying with HIPAA/GDPR through two technical safeguards: differential privacy via DP-SGD during local training and secure aggregation of model updates. Using LSTM/GRU architectures optimized for sequential medical data, the framework achieves an F1 Score of 67% with precision (60%) and recall (75%) suitable for

APA, Harvard, Vancouver, ISO, and other styles

40

Kolluru, Vinothkumar, Yudhisthir Nuthakki, Sudeep Mungara, Sonika Koganti, Advaitha Naidu Chintakunta, and Charan Sundar Telaganeni. "Healthcare Through AI: Integrating Deep Learning, Federated Learning, and XAI for Disease Management." International Journal of Soft Computing and Engineering 13, no. 6 (2024): 21–27. http://dx.doi.org/10.35940/ijsce.d3646.13060124.

Full text

Abstract:

The applications of Artificial Intelligence (AI) have been resonating across various fields for the past three decades, with the healthcare domain being a primary beneficiary of these innovations and advancements. Recently, AI techniques such as deep learning, machine learning, and federated learning have been frequently employed to address challenges in disease management. However, these techniques often face issues related to transparency, interpretability, and explainability. This is where explainable AI (XAI) plays a crucial role in ensuring the explainability of AI models. There is a need

APA, Harvard, Vancouver, ISO, and other styles

41

Abdel-Jaber, Hussein, Disha Devassy, Azhar Al Salam, Lamya Hidaytallah, and Malak EL-Amir. "A Review of Deep Learning Algorithms and Their Applications in Healthcare." Algorithms 15, no. 2 (2022): 71. http://dx.doi.org/10.3390/a15020071.

Full text

Abstract:

Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data. It was found that deep learning approaches can be used for big data analysis successfully. Applications include virtual assistants such as A

APA, Harvard, Vancouver, ISO, and other styles

42

Chattopadhyay, Soumiki, and Souti Chattopadhyay. "A Deep Learning Enabled Chatbot Approach for Self-Diagnosis." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1810–18. http://dx.doi.org/10.22214/ijraset.2022.48322.

Full text

Abstract:

Abstract: Healthcare is an essential need that is often inaccessible or unavailable. With rising populations and deadly viruses, the load on healthcare workers is the highest, while the ratio of healthcare workers per person has hit an all time low. This extreme demand for assistance in the face of unavailability has opened up avenues for computer assisted healthcare. In this paper, we propose an approach to use advancements in computer assisted healthcare in two directions. We employ machine learning algorithms to predict the disease correctly. We used Random Forest and XGBoost as this system

APA, Harvard, Vancouver, ISO, and other styles

43

Krčadinac, Olja, and Nebojša Stojaković. "The Application of Deep Learning in Medicine: Benefits, Challenges, and Future Prospects." Journal of UUNT: Informatics and Computer Sciences 1, no. 1 (2024): 19–25. https://doi.org/10.62907/juuntics240101019s.

Full text

Abstract:

Deep learning has emerged as a transformative technology in the field of medicine, offering numerous advantages in the diagnosis, treatment, and management of healthcare. This paper explores the application of deep learning in medicine, highlighting its benefits, challenges, and future prospects. The advantages of deep learning include enhanced accuracy and efficiency in diagnosing medical conditions, automation of administrative tasks, support for personalized and preventive care, and the potential for early disease detection and improved treatment outcomes. However, the use of deep learning

APA, Harvard, Vancouver, ISO, and other styles

44

Bordoloi, Dibyahash, Vijay Singh, Sumaya Sanober, Seyed Mohamed Buhari, Javed Ahmed Ujjan, and Rajasekhar Boddu. "Deep Learning in Healthcare System for Quality of Service." Journal of Healthcare Engineering 2022 (March 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/8169203.

Full text

Abstract:

Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and sm

APA, Harvard, Vancouver, ISO, and other styles

45

Loftus, Tyler J., Benjamin Shickel, Matthew M. Ruppert, et al. "Uncertainty-aware deep learning in healthcare: A scoping review." PLOS Digital Health 1, no. 8 (2022): e0000085. http://dx.doi.org/10.1371/journal.pdig.0000085.

Full text

Abstract:

Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment could be earned by conveying model certainty, or the probability that a given model output is accurate, but the use of uncertainty estimation for deep learning entrustment is largely unexplored, and there is no consensus regarding optimal methods for quantifying uncertainty. Our purpose is to critically evaluate methods for quantifying uncertainty in deep learning for healthcare applications and propose a conceptual framework for specifying certainty of deep learning predictions. We searched Embase, M

APA, Harvard, Vancouver, ISO, and other styles

46

Alsaheel, Alaa, Reem Alhassoun, Reema Alrashed, Noura Almatrafi, Noura Almallouhi, and Saleh Albahli. "Deep Fakes in Healthcare: How Deep Learning Can Help to Detect Forgeries." Computers, Materials & Continua 76, no. 2 (2023): 2461–82. http://dx.doi.org/10.32604/cmc.2023.040257.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Nasiruddin, Md, Mohammad Abir Hider, Rabeya Akter, et al. "OPTIMIZING SKIN CANCER DETECTION IN THE USA HEALTHCARE SYSTEM USING DEEP LEARNING AND CNNS." American Journal of Medical Sciences and Pharmaceutical Research 06, no. 12 (2024): 92–112. https://doi.org/10.37547/tajmspr/volume06issue12-10.

Full text

Abstract:

Skin cancer is among the most prevalent cancers in the USA, with millions of new cases reported each year. The two main types of skin cancer include aggressive, life-threatening melanoma and less lethal, though potentially very morbid if left unattended, non-melanoma types: basal cell carcinoma and squamous cell carcinoma. The chief aim of this research project is to devise, curate, and propose a deep-learning CNN methodology for skin cancer detection in the USA. The dataset for the current research project was retrieved from the Kaggle website, particularly, The ISIC 2016 Skin Cancer Dataset

APA, Harvard, Vancouver, ISO, and other styles

48

Vasudeva, R., and S. N. Chandrashekara. "An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning." Indian Journal Of Science And Technology 16, no. 35 (2023): 2796–806. http://dx.doi.org/10.17485/ijst/v16i35.945.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Hassan, Edidiong. "Integrating Deep Learning and Big Data to Enhance Predictive Analytics in Healthcare Decision Making." International Journal of Research Publication and Reviews 6, no. 4 (2025): 817–33. https://doi.org/10.55248/gengpi.6.0425.1332.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Lufyagila, Beston, and Francis A. Ruambo. "Exploring the Potential of Deep Learning in Healthcare: A perspective." East African Journal of Interdisciplinary Studies 7, no. 1 (2024): 80–88. http://dx.doi.org/10.37284/eajis.7.1.1947.

Full text

Abstract:

Deep learning has received much interest in the field of healthcare in recent years. Health care plays a significant role in delivering services and practices that promote, maintain, and restore health of an individual. However, applying deep learning in healthcare is still an exciting area of research. This paper explores the application of deep learning, and henceforth, it highlights new perspectives in healthcare by reviewing published state-of-the-art research works from four scholarly databases, including Scopus, Web of Science, Pubmed, and Google Scholar. The selected studies were from A

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!