Medical image analysis using deep learning algorithms
In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medic...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-11-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1273253/full |
| _version_ | 1827768835367763968 |
|---|---|
| author | Mengfang Li Yuanyuan Jiang Yanzhou Zhang Haisheng Zhu |
| author_facet | Mengfang Li Yuanyuan Jiang Yanzhou Zhang Haisheng Zhu |
| author_sort | Mengfang Li |
| collection | DOAJ |
| description | In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability. |
| first_indexed | 2024-03-11T12:17:20Z |
| format | Article |
| id | doaj.art-15f2ba0e806e49258ef88104b61a1307 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-11T12:17:20Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-15f2ba0e806e49258ef88104b61a13072023-11-07T07:21:35ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-11-011110.3389/fpubh.2023.12732531273253Medical image analysis using deep learning algorithmsMengfang Li0Yuanyuan Jiang1Yanzhou Zhang2Haisheng Zhu3The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Cardiovascular Medicine, Wencheng People’s Hospital, Wencheng, ChinaIn the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1273253/fulldeep learningmachine learningmedical imagesimage analysisconvolutional neural networks |
| spellingShingle | Mengfang Li Yuanyuan Jiang Yanzhou Zhang Haisheng Zhu Medical image analysis using deep learning algorithms Frontiers in Public Health deep learning machine learning medical images image analysis convolutional neural networks |
| title | Medical image analysis using deep learning algorithms |
| title_full | Medical image analysis using deep learning algorithms |
| title_fullStr | Medical image analysis using deep learning algorithms |
| title_full_unstemmed | Medical image analysis using deep learning algorithms |
| title_short | Medical image analysis using deep learning algorithms |
| title_sort | medical image analysis using deep learning algorithms |
| topic | deep learning machine learning medical images image analysis convolutional neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1273253/full |
| work_keys_str_mv | AT mengfangli medicalimageanalysisusingdeeplearningalgorithms AT yuanyuanjiang medicalimageanalysisusingdeeplearningalgorithms AT yanzhouzhang medicalimageanalysisusingdeeplearningalgorithms AT haishengzhu medicalimageanalysisusingdeeplearningalgorithms |