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Bioengineering
Special Issues
Artificial Intelligence for Skin Diseases Classification
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Artificial Intelligence for Skin Diseases Classification

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2932

Special Issue Editors

Dr. Alessandra Cartocci

E-Mail Website
Guest Editor
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
Interests: applied biostatistics; statistical analysis; data analysis; skin diseases
Prof. Dr. Linda Tognetti

E-Mail Website
Guest Editor
Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
Interests: dermato-oncology; melanoma; non-melanoma skin cancer; noninvasive skin imaging; artificial intelligence; pediatric dermatology; translational dermo-research; photobiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has exhibited remarkable potential in dermatology, particularly in the classification and diagnosis of skin diseases. AI techniques, such as machine learning, deep learning, and computer vision, have led to significant advancements in the accurate identification and classification of various skin conditions. These technologies can analyze large datasets and can enhance the quality of the care provided to patients.

This Special Issue focuses on the use of artificial intelligence for the classification of skin diseases. Topics of interest include, but are not limited to, the development of AI algorithms for skin lesion detection, the classification of different types of skin diseases, and the integration of AI systems into clinical practice.

We invite researchers, scientists, and healthcare professionals to contribute their original work to this Special Issue. To advance the field of dermatology via the innovative use of artificial intelligence, we aim to present the latest advancements in AI technology and dermatology that enhance the diagnosis and treatment outcomes for patients with skin diseases.

Dr. Alessandra Cartocci
Prof. Dr. Linda Tognetti
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

  • artificial intelligence
  • dermatology
  • skin diseases classification
  • machine learning
  • deep learning
  • computer vision
  • skin lesion detection
  • healthcare

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Published Papers (2 papers)

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Research

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24 pages, 2586 KiB  
Article
Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition
by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou and Chao Xin
Bioengineering 2025, 12(3), 282; https://doi.org/10.3390/bioengineering12030282 - 12 Mar 2025
Cited by 1 | Viewed by 694
Abstract
The rising prevalence of skin lesions places a heavy burden on global health resources and necessitates an early and precise diagnosis for successful treatment. The diagnostic potential of recent multi-modal skin lesion detection algorithms is limited because they ignore dynamic interactions and information [...] Read more.
The rising prevalence of skin lesions places a heavy burden on global health resources and necessitates an early and precise diagnosis for successful treatment. The diagnostic potential of recent multi-modal skin lesion detection algorithms is limited because they ignore dynamic interactions and information sharing across modalities at various feature scales. To address this, we propose a deep learning framework, Multi-Modal Skin-Imaging-based Information-Switching Network (MDSIS-Net), for end-to-end skin lesion recognition. MDSIS-Net extracts intra-modality features using transfer learning in a multi-scale fully shared convolutional neural network and introduces an innovative information-switching module. A cross-attention mechanism dynamically calibrates and integrates features across modalities to improve inter-modality associations and feature representation in this module. MDSIS-Net is tested on clinical disfiguring dermatosis data and the public Derm7pt melanoma dataset. A Visually Intelligent System for Image Analysis (VISIA) captures five modalities: spots, red marks, ultraviolet (UV) spots, porphyrins, and brown spots for disfiguring dermatosis. The model performs better than existing approaches with an mAP of 0.967, accuracy of 0.960, precision of 0.935, recall of 0.960, and f1-score of 0.947. Using clinical and dermoscopic pictures from the Derm7pt dataset, MDSIS-Net outperforms current benchmarks for melanoma, with an mAP of 0.877, accuracy of 0.907, precision of 0.911, recall of 0.815, and f1-score of 0.851. The model’s interpretability is proven by Grad-CAM heatmaps correlating with clinical diagnostic focus areas. In conclusion, our deep multi-modal information-switching model enhances skin lesion identification by capturing relationship features and fine-grained details across multi-modal images, improving both accuracy and interpretability. This work advances clinical decision making and lays a foundation for future developments in skin lesion diagnosis and treatment. Full article
(This article belongs to the Special Issue Artificial Intelligence for Skin Diseases Classification)
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Review

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25 pages, 1441 KiB  
Review
Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present
by Linda Tognetti, Chiara Miracapillo, Simone Leonardelli, Alessio Luschi, Ernesto Iadanza, Gabriele Cevenini, Pietro Rubegni and Alessandra Cartocci
Bioengineering 2024, 11(8), 758; https://doi.org/10.3390/bioengineering11080758 - 26 Jul 2024
Cited by 2 | Viewed by 1566
Abstract
There has been growing scientific interest in the research field of deep learning techniques applied to skin cancer diagnosis in the last decade. Though encouraging data have been globally reported, several discrepancies have been observed in terms of study methodology, result presentations and [...] Read more.
There has been growing scientific interest in the research field of deep learning techniques applied to skin cancer diagnosis in the last decade. Though encouraging data have been globally reported, several discrepancies have been observed in terms of study methodology, result presentations and validation in clinical settings. The present review aimed to screen the scientific literature on the application of DL techniques to dermoscopic melanoma/nevi differential diagnosis and extrapolate those original studies adequately by reporting on a DL model, comparing them among clinicians and/or another DL architecture. The second aim was to examine those studies together according to a standard set of statistical measures, and the third was to provide dermatologists with a comprehensive explanation and definition of the most used artificial intelligence (AI) terms to better/further understand the scientific literature on this topic and, in parallel, to be updated on the newest applications in the medical dermatologic field, along with a historical perspective. After screening nearly 2000 records, a subset of 54 was selected. Comparing the 20 studies reporting on convolutional neural network (CNN)/deep convolutional neural network (DCNN) models, we have a scenario of highly performant DL algorithms, especially in terms of low false positive results, with average values of accuracy (83.99%), sensitivity (77.74%), and specificity (80.61%). Looking at the comparison with diagnoses by clinicians (13 studies), the main difference relies on the specificity values, with a +15.63% increase for the CNN/DCNN models (average specificity of 84.87%) compared to humans (average specificity of 64.24%) with a 14,85% gap in average accuracy; the sensitivity values were comparable (79.77% for DL and 79.78% for humans). To obtain higher diagnostic accuracy and feasibility in clinical practice, rather than in experimental retrospective settings, future DL models should be based on a large dataset integrating dermoscopic images with relevant clinical and anamnestic data that is prospectively tested and adequately compared with physicians. Full article
(This article belongs to the Special Issue Artificial Intelligence for Skin Diseases Classification)
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