Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
3.8
Journals
Bioengineering
Special Issues
Application of Deep Learning in Medical Diagnosis
share announcement

Application of Deep Learning in Medical Diagnosis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3906

Special Issue Editors

Prof. Dr. Zhanlin Ji

E-Mail Website
Guest Editor
1. Hebei Key Laboratory of Industrial Intelligent Perception, North China University of Science and Technology, Tangshan 063210, China
2. Telecommunications Research Centre (TRC), University of Limerick, V94 T9PX Limerick, Ireland
Interests: the ubiquitous consumer wireless world (UCWW); the Internet of Things (IoT); cloud computing; big data management; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Li Zhao

E-Mail Website
Guest Editor
The Speech and Language Technology Team (CSLT) of the National Research Center for Information Science and Technology in Beijing, Tsinghua University, Beijing, China
Interests: mobile computing; Internet of Things (IoT); e-health systems; intelligent transportation systems (ITS); home networking; machine learning; digital multimedia

Special Issue Information

Dear Colleagues,

The application of deep learning in medical diagnostics is revolutionizing the field of medicine. Especially in medical imaging diagnosis, the introduction of deep learning methods has had a profound impact on the workflow from image acquisition to diagnostic reporting. These methods excel at automating tedious image analysis tasks and efficiently processing large and complex medical image datasets.

The interventions of deep learning and artificial intelligence provide new possibilities for accelerating medical diagnosis. As hardware and algorithms continue to advance, researchers are better able to understand and predict a patient's condition and link medical images to disease features. Deep learning has shown great potential in the interpretation of medical images, such as in foci segmentation, cancer detection, disease classification, and so on.

However, it is important to note that the correct application of deep learning is crucial in medical diagnosis. With deep learning, the data are analyzed and interpreted with the help of computer-expanded knowledge. The impact of these tools is huge, and the use of AI is helping many stakeholders in the field of smart healthcare. The future of applying deep learning in medical diagnostics is exciting, promising not only to improve diagnostic accuracy but also to potentially provide more precise information for personalized medicine and treatment planning. Developments in this field are pushing medical science to new heights to provide better medical care for patients.

The range of research topics may include, but are not limited to:

  • Medical image processing;
  • Focus segmentation based on medical images;
  • Lesion target detection based on medical images;
  • Classification of lesion grade and category in medical images;
  • Natural language processing and knowledge discovery in medical documentation;
  • Biomedical image reconstruction;
  • Automatic/computer-aided diagnosis based on deep learning.

Prof. Dr. Zhanlin Ji
Dr. Li Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • artificial intelligence
  • medical image segmentation
  • medical image target detection
  • medical image classification
  • computer-assisted therapy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 5200 KiB  
Article
Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms
by Chun-Yu Lin, Jacky Chung-Hao Wu, Yen-Ming Kuan, Yi-Chun Liu, Pi-Yi Chang, Jun-Peng Chen, Henry Horng-Shing Lu and Oscar Kuang-Sheng Lee
Bioengineering 2024, 11(4), 399; https://doi.org/10.3390/bioengineering11040399 - 19 Apr 2024
Cited by 1 | Viewed by 1693
Abstract
Background and objective: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to [...] Read more.
Background and objective: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. Methods: In this retrospective study, CT images of 1070 T3–4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. Results: The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model’s capability to predict adverse clinical outcomes was superior to those of traditional assessments. Conclusion: AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques. Full article
(This article belongs to the Special Issue Application of Deep Learning in Medical Diagnosis)
►▼ Show Figures

Graphical abstract

15 pages, 2250 KiB  
Article
DTONet a Lightweight Model for Melanoma Segmentation
by Shengnan Hao, Hongzan Wang, Rui Chen, Qinping Liao, Zhanlin Ji, Tao Lyu and Li Zhao
Bioengineering 2024, 11(4), 390; https://doi.org/10.3390/bioengineering11040390 - 18 Apr 2024
Viewed by 1488
Abstract
With the further development of neural networks, automatic segmentation techniques for melanoma are becoming increasingly mature, especially under the conditions of abundant hardware resources. This allows for the accuracy of segmentation to be improved by increasing the complexity and computational capacity of the [...] Read more.
With the further development of neural networks, automatic segmentation techniques for melanoma are becoming increasingly mature, especially under the conditions of abundant hardware resources. This allows for the accuracy of segmentation to be improved by increasing the complexity and computational capacity of the model. However, a new problem arises when it comes to actual applications, as there may not be the high-end hardware available, especially in hospitals and among the general public, who may have limited computing resources. In response to this situation, this paper proposes a lightweight deep learning network that can achieve high segmentation accuracy with minimal resource consumption. We introduce a network called DTONet (double-tailed octave network), which was specifically designed for this purpose. Its computational parameter count is only 30,859, which is 1/256th of the mainstream UNet model. Despite its reduced complexity, DTONet demonstrates superior performance in terms of accuracy, with an IOU improvement over other similar models. To validate the generalization capability of this model, we conducted tests on the PH2 dataset, and the results still outperformed existing models. Therefore, the proposed DTONet network exhibits excellent generalization ability and is sufficiently outstanding. Full article
(This article belongs to the Special Issue Application of Deep Learning in Medical Diagnosis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop