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AI-Driven Innovations in Healthcare: Advances in Machine Learning and Computer...
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AI-Driven Innovations in Healthcare: Advances in Machine Learning and Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1350

Special Issue Editors

Dr. Zhi-Ri Tang

E-Mail Website
Guest Editor
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519000, China
Interests: machine learning; computer vision; biomedical engineering; AI for healthcare
Dr. Ruihan Hu

E-Mail Website
Guest Editor
School of Software, Harbin Institute of Technology, Harbin 150001, China
Interests: distributed parallel computing; cloud-side collaborative computing; multi-embodied intelligence collaborative computing in healthcare

Special Issue Information

Dear Colleagues,

The rapid development of artificial intelligence (AI) technology has elicited unprecedented and profound changes in the healthcare field. This change is reflected in many ways, such as the adoption of machine learning algorithms, to clinical discrete data and computer vision technologies, to medical image processing. AI can not only improve the accuracy and efficiency of medical diagnoses and provide doctors with reliable auxiliary diagnoses, but also optimizes the patient's medical experience through intelligent medical consultation and telemedicine. Furthermore, it could provide strong technical support for disease prevention, treatment, and new drug research and development. There is broad potential for AI in the healthcare field that is worth exploring, including the theoretical aspects of AI algorithms, mathematical proof of interpretability, and computational aspects of applications in healthcare.

This Special Issue will focus on recent AI applications in healthcare, especially in terms of theory, and the mathematical aspects of advanced machine learning and computer vision methods for solving practical healthcare problems. Relevant topics include, but are not limited to, the following:

  • The theory and mathematical aspects of AI in screening and diagnosis;
  • The theory and mathematical aspects of AI in drug R&D;
  • The theory and mathematical aspects of AI in infectious disease prevention and control;
  • The theory and mathematical aspects of AI in personalized medicine;
  • The theory and mathematical aspects of AI in telemedicine;
  • The theory and mathematical aspects of medical data privacy and security;
  • The theory and mathematical aspects of AI system bias in healthcare;
  • The interpretability of AI in healthcare;

We also welcome review papers emphasizing recent scientific advancements in the use of AI in healthcare.

Dr. Zhi-Ri Tang
Dr. Ruihan Hu
Guest Editors

Ms. Xinyi Liu
Guest Editor Assistant
Affiliation: School of Intelligent Systems Science and Engineering, Jinan University, Jinan, China
Email:
Interests: computer vision; biomedical engineering; AI for healthcare

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

  • artificial intelligence
  • healthcare
  • machine learning
  • computer vision

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Published Papers (3 papers)

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Research

36 pages, 12865 KiB  
Article
Enhancing Recognition and Categorization of Skin Lesions with Tailored Deep Convolutional Networks and Robust Data Augmentation Techniques
by Syed Ibrar Hussain and Elena Toscano
Mathematics 2025, 13(9), 1480; https://doi.org/10.3390/math13091480 (registering DOI) - 30 Apr 2025
Abstract
This study introduces deep convolutional neural network-based methods for the detection and classification of skin lesions, enhancing system accuracy through a combination of architectures, pre-processing techniques and data augmentation. Multiple networks, including XceptionNet, DenseNet, MobileNet, NASNet Mobile, and EfficientNet, were evaluated to test [...] Read more.
This study introduces deep convolutional neural network-based methods for the detection and classification of skin lesions, enhancing system accuracy through a combination of architectures, pre-processing techniques and data augmentation. Multiple networks, including XceptionNet, DenseNet, MobileNet, NASNet Mobile, and EfficientNet, were evaluated to test deep learning’s potential in complex, multi-class classification tasks. Training these models on pre-processed datasets with optimized hyper-parameters (e.g., batch size, learning rate, and dropout) improved classification precision for early-stage skin cancers. Evaluation measures such as accuracy and loss confirmed high classification efficiency with minimal overfitting, as the validation results aligned closely with training. DenseNet-201 and MobileNet-V3 Large demonstrated strong generalization abilities, whereas EfficientNetV2-B3 and NASNet Mobile achieved the best balance between accuracy and efficiency. The application of different augmentation rates per class also enhanced the handling of imbalanced data, resulting in more accurate large-scale detection. Comprehensive pre-processing ensured balanced class representation, and EfficientNetV2 models achieved exceptional classification accuracy, attributed to their optimized architecture balancing depth, width, and resolution. These models showed high convergence rates and generalization, supporting their suitability for medical imaging tasks using transfer learning. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Healthcare: Advances in Machine Learning and Computer Vision)
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20 pages, 3550 KiB  
Article
Ambiance Preservation Augmenting for Semantic Segmentation of Pediatric Burn Skin Lesions
by Laura Florea, Corneliu Florea, Constantin Vertan and Silviu Bădoiu
Mathematics 2025, 13(5), 758; https://doi.org/10.3390/math13050758 - 25 Feb 2025
Viewed by 425
Abstract
Burn injuries pose a significant threat to human life, with high morbidity and mortality rates. Accurate diagnosis, including the assessment of burn area and depth, is essential for effective treatment and can sometimes be lifesaving. However, access to specialized medical professionals is often [...] Read more.
Burn injuries pose a significant threat to human life, with high morbidity and mortality rates. Accurate diagnosis, including the assessment of burn area and depth, is essential for effective treatment and can sometimes be lifesaving. However, access to specialized medical professionals is often limited, particularly in remote or underserved regions. To address this challenge and alleviate the burden on healthcare providers, researchers are investigating automated diagnostic tools. The severity of the burn and the affected body surface area are critical factors in diagnosis. From a computer vision perspective, this requires semantic segmentation of burn images to assess the affected area and determine burn severity. In collaboration with medical personnel, we have gathered a dataset of in situ images from a local children’s hospital annotated by specialist burn surgeons. However, due to the limited amount of data, we propose a two-step augmentation approach: training with synthetic burn images and controlling the encoder by ambiance preservation. The latter is a technique that forces the encoder to represent closely the embeddings of images that are similar and is a key contribution of this paper. The method is evaluated on the BAMSI database, demonstrating that the proposed augmentations lead to better performance compared with strong baselines and other potential algorithmic improvements. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Healthcare: Advances in Machine Learning and Computer Vision)
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21 pages, 2555 KiB  
Article
FldtMatch: Improving Unbalanced Data Classification via Deep Semi-Supervised Learning with Self-Adaptive Dynamic Threshold
by Xin Wu, Jingjing Xu, Kuan Li, Jianping Yin and Jian Xiong
Mathematics 2025, 13(3), 392; https://doi.org/10.3390/math13030392 - 24 Jan 2025
Viewed by 651
Abstract
Among the many methods of deep semi-supervised learning (DSSL), the holistic method combines ideas from other methods, such as consistency regularization and pseudo-labeling, with great success. This method typically introduces a threshold to utilize unlabeled data. If the highest predictive value from unlabeled [...] Read more.
Among the many methods of deep semi-supervised learning (DSSL), the holistic method combines ideas from other methods, such as consistency regularization and pseudo-labeling, with great success. This method typically introduces a threshold to utilize unlabeled data. If the highest predictive value from unlabeled data exceeds the threshold, the associated class is designated as the data’s pseudo-label. However, current methods utilize fixed or dynamic thresholds, disregarding the varying learning difficulties across categories in unbalanced datasets. To overcome these issues, in this paper, we first designed Cumulative Effective Labeling (CEL) to reflect a particular class’s learning difficulty. This approach differs from previous methods because it uses effective pseudo-labels and ground truth, collectively influencing the model’s capacity to acquire category knowledge. In addition, based on CEL, we propose a simple but effective way to compute the threshold, Self-adaptive Dynamic Threshold (SDT). It requires a single hyperparameter to adjust to various scenarios, eliminating the necessity for a unique threshold modification approach for each case. SDT utilizes a clever mapping function that can solve the problem of differential learning difficulty of various categories in an unbalanced image dataset that adversely affects dynamic thresholding. Finally, we propose a deep semi-supervised method with SDT called FldtMatch. Through theoretical analysis and extensive experiments, we have fully proven that FldtMatch can overcome the negative impact of unbalanced data. Regardless of the choice of the backbone network, our method achieves the best results on multiple datasets. The maximum improvement of the macro F1-Score metric is about 5.6% in DFUC2021 and 2.2% in ISIC2018. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Healthcare: Advances in Machine Learning and Computer Vision)
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