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Mathematics
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Advanced Applications of Deep Learning Methods in Medical Diagnosis
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Advanced Applications of Deep Learning Methods in Medical Diagnosis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 632

Special Issue Editors

Dr. Chao Tong

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Beihang University, Beijing, China
Interests: mobile computing; complex networks; machine learning; data mining; image processing
Dr. Xiaojun Wang

E-Mail Website
Guest Editor
School of Electronic Engineering, Dublin City University, Collins Avenue Extension, D09 D209 Dublin, Ireland
Interests: green communication and networking; network security; hardware acceleration for ML and A; AI technology in education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A multitude of medical data from patients provide valuable information for diagnosing various diseases. However, it is cumbersome for clinicians to examine these data manually. In recent years, with advances in computer technology and mathematical methods, deep learning methods have been widely used in medical diagnosis; thus, medical diagnosis based on deep learning has become an important research direction.

This Special Issue, entitled "Advanced Applications of Deep Learning Methods in Medical Diagnosis", aims to highlight the latest advances in the field of deep learning for medical diagnosis. We invite authors to submit original research articles as well as review articles, focusing on (but not limited to) the following topics:

  • Deep learning of multimodal medical data;
  • Deep learning for lesion recognition and localization in medical images;
  • Deep learning for medical image processing;
  • Interpretability of medical diagnosis in deep learning;
  • Semi-supervised learning in medical diagnosis;
  • Medical generation models;
  • Computer-aided diagnosis systems based on deep learning.

Dr. Chao Tong
Dr. Xiaojun Wang
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. Mathematics 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 2600 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
  • machine learning
  • medical diagnosis
  • medical data
  • medical image processing
  • medical system

Published Papers (1 paper)

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Research

16 pages, 2866 KiB  
Article
Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach
by Turki Turki, Sarah Al Habib and Y-h. Taguchi
Mathematics 2024, 12(10), 1573; https://doi.org/10.3390/math12101573 - 17 May 2024
Viewed by 447
Abstract
Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy [...] Read more.
Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy and infected human alveolar type II (hAT2) cells with SARS-CoV-2. Hence, we explore the feasibility of deep transfer learning (DTL) and introduce a highly accurate approach that works as follows: First, we downloaded and processed 286 images pertaining to healthy and infected hAT2 cells obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than one million images from the ImageNet database to extract features from hAT2 images. Then, it flattens and provides the output feature vectors to a trained, densely connected classifier with the Adam optimizer. The second DTL computation works in a similar manner, with a minor difference in that we freeze the first layers for feature extraction in pre-trained models while unfreezing and jointly training the next layers. The results using five-fold cross-validation demonstrated that TFeDenseNet201 is 12.37× faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance (P<2.2×10−16 from a t-test) compared to an average ACC of 0.937 (F1 of 0.938 and MCC of 0.877) for the counterpart (TFtDenseNet201), showing no significance results (P=0.093 from a t-test). Full article
(This article belongs to the Special Issue Advanced Applications of Deep Learning Methods in Medical Diagnosis)
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