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Electronics
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Deep Learning Algorithms and Their Application to Medical Image Analysis
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Deep Learning Algorithms and Their Application to Medical Image Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 2984

Special Issue Editors

Dr. Wei Shen

E-Mail Website
Guest Editor
Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: computer vision; deep learning; medical image analysis
Prof. Dr. Xinggang Wang

E-Mail Website
Guest Editor
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: computer vision and machine learning, especially data/label- and computation-efficient deep learning for visual recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenyu Liu

E-Mail Website
Guest Editor
Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: compuetr vision; artificial intelligence
Prof. Dr. Yan Xu

E-Mail Website
Guest Editor
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
Interests: machine learning; medical data mining; medical imaging processing; text mining

Special Issue Information

Dear Colleagues,

Medical image analysis serves as a substantial tool to assist radiologists and doctors. Deep learning has been widely used for medical image analysis and has achieved promising performance due to its outstanding automatic feature learning ability. We are witnessing the rapid revolution of deep learning in medical applications, such as computer-aided diagnosis, computer-aided surgery, and radiation therapy. The aim of this Special Issue is to develop novel deep learning algorithms to address medical image analysis problems, such as image registration, anatomical/cell structure detection, tissue segmentation, and disease diagnosis or prognosis. We hope readers that will enjoy this Special Issue and that the articles within will have a significant impact on the field.

Dr. Wei Shen
Prof. Dr. Xinggang Wang
Prof. Dr. Wenyu Liu
Prof. Dr. Yan Xu
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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
  • medical image analysis
  • computer-aided diagnosis
  • image registration
  • segmentation
  • detection

Published Papers (2 papers)

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Research

15 pages, 2890 KiB  
Article
RED-CNN: The Multi-Classification Network for Pulmonary Diseases
by San-Li Yi, Sheng-Lin Qin, Fu-Rong She and Tian-Wei Wang
Electronics 2022, 11(18), 2896; https://doi.org/10.3390/electronics11182896 - 13 Sep 2022
Cited by 1 | Viewed by 1231
Abstract
Deep learning is a convenient method for doctors to classify pulmonary diseases such as COVID-19, viral pneumonia, bacterial pneumonia, and tuberculosis. However, such a task requires a dataset including samples of all these diseases and a more effective network to capture the features [...] Read more.
Deep learning is a convenient method for doctors to classify pulmonary diseases such as COVID-19, viral pneumonia, bacterial pneumonia, and tuberculosis. However, such a task requires a dataset including samples of all these diseases and a more effective network to capture the features of images accurately. In this paper, we propose a five-classification pulmonary disease model, including the pre-processing of input data, feature extraction, and classifier. The main points of this model are as follows. Firstly, we present a new network named RED-CNN which is based on CNN architecture and constructed using the RED block. The RED block is composed of the Res2Net module, ECA module, and Double BlazeBlock module, which are capable of extracting more detailed information, providing cross-channel information, and enhancing the extraction of global information with strong feature extraction capability. Secondly, by merging two selected datasets, the Curated Chest X-Ray Image Dataset for COVID-19 and the tuberculosis (TB) chest X-ray database, we constructed a new dataset including five types of data: normal, COVID-19, viral pneumonia, bacterial pneumonia, and tuberculosis. In order to assess the efficiency of the proposed five-classification model, a series of experiments based on the new dataset were carried out and based on 5-fold cross validation, and the results of the accuracy, precision, recall, F1 value, and Jaccard scores of the proposed method were 91.796%, 92.062%, 91.796%, 91.892%, and 86.176%, respectively. Our proposed algorithm performs better than other classification algorithms. Full article
(This article belongs to the Special Issue Deep Learning Algorithms and Their Application to Medical Image Analysis)
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11 pages, 4498 KiB  
Article
Enhanced Automatic Morphometry of Nerve Histological Sections Using Ensemble Learning
by Yazan Dweiri, Mousa Al-Zanina and Dominique Durand
Electronics 2022, 11(14), 2277; https://doi.org/10.3390/electronics11142277 - 21 Jul 2022
Viewed by 1201
Abstract
There is a need for an automated morphometry algorithm to facilitate the otherwise labor-intensive task of the quantitative histological analysis of neural microscopic images. A benchmark morphometry algorithm is the convolutional neural network Axondeepseg (ADS), which yields a high segmentation accuracy for scanning [...] Read more.
There is a need for an automated morphometry algorithm to facilitate the otherwise labor-intensive task of the quantitative histological analysis of neural microscopic images. A benchmark morphometry algorithm is the convolutional neural network Axondeepseg (ADS), which yields a high segmentation accuracy for scanning and transmission electron microscopy images. Nevertheless, it shows decreased accuracy when applied to optical microscopy images, and it has been observed to yield sizable false positives when identifying small-sized neurons within the slides. In this study, ensemble learning is used to enhance the performance of ADS by combining it with the paired image-to-image translation algorithm PairedImageTranslation (PIT). Here, 120 optical microscopy images of peripheral nerves were used to train and test the ensemble learning model and the two base models individually for comparison. The results showed weighted pixel-wise accuracy for the ensemble model of 95.5%, whereas the ADS and PIT yielded accuracies of 93.4% and 90%, respectively. The automated measurements of the axon diameters and myelin thicknesses from the manually marked ground truth images were not statistically different (p = 0.05) from the measurements taken from the same images when segmented using the developed ensemble model, while they were different when they were measured from the segmented images by the two base models individually. The automated measurement of the G ratios indicated a higher similarity to the ground truth testing images for the ensemble model in comparison with the individual base models. The proposed model yielded automated segmentation of the nerve slides, which were sufficiently equivalent to the manual annotations and could be employed for axon diameters and myelin thickness measurements for fully automated histological analysis of the neural images. Full article
(This article belongs to the Special Issue Deep Learning Algorithms and Their Application to Medical Image Analysis)
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