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Bioengineering
Special Issues
Interpretable Deep Learning for Object Detection and Medical Image Classification
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Interpretable Deep Learning for Object Detection and Medical Image Classification

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

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 681

Special Issue Editor

Dr. Gurmail Singh

E-Mail Website
Guest Editor
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: machine learning; artificial intelligence; computational learning theory; computer vision; natural language processing; reality-based algebras; Hopf algebra; representation theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning models are well-known for their ability to solve complex problems. Currently, deep learning models based on convolutional neural networks (CNNs) and the vision transformer (ViT) are being increasingly used for image classification. Additionally, models such as you only look once (YOLO) are also known for their object detection and image segmentation capabilities. However, interpretable deep learning models are crucial for high-stakes decision-making applications, for example, medical image classification.

This Special Issue will publish high-quality original research papers and comprehensive surveys on all aspects surrounding “Interpretable Deep Learning for Object Detection and Medical Image Classification”. We invite researchers and practitioners interested in the above topic to submit their manuscripts for consideration in this Special Issue.

Dr. Gurmail Singh
Guest Editor

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

  • machine learning
  • deep learning
  • pattern recognition
  • neural networks
  • medical images
  • X-rays and CT-scan images
  • object detection and recognition
  • image segmentation
  • image classification

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Published Papers (1 paper)

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Research

19 pages, 2261 KB  
Article
Prognostic Evaluation of Lower Third Molar Eruption Status from Panoramic Radiographs Using Artificial Intelligence-Supported Machine and Deep Learning Models
by Ipek N. Guldiken, Alperen Tekin, Tunahan Kanbak, Emine N. Kahraman and Mutlu Özcan
Bioengineering 2025, 12(11), 1176; https://doi.org/10.3390/bioengineering12111176 - 29 Oct 2025
Viewed by 449
Abstract
The prophylactic extraction of third molars is highly dependent on the surgeon’s experience as the common practices and guidelines contradict. The purpose of this study was to evaluate the eruption status of impacted third molars using deep learning-based artificial intelligence (AI) and to [...] Read more.
The prophylactic extraction of third molars is highly dependent on the surgeon’s experience as the common practices and guidelines contradict. The purpose of this study was to evaluate the eruption status of impacted third molars using deep learning-based artificial intelligence (AI) and to develop a model that predicts their final positions at an early stage to aid clinical decisions. In this retrospective study, 1102 panoramic radiographs (PANs) were annotated by three expert dentists to classify eruption status as either initial or definitive. A dataset was created and two deep learning architectures, InceptionV3 and ResNet50, were tested through a three-phase protocol: hyperparameter tuning, model evaluation, and assessment of preprocessing effects. Accuracy, recall, precision, and F1 score were used as performance metrics. Classical machine learning (ML) algorithms (SVM, KNN, and logistic regression) were also applied to features extracted from the deep models. ResNet50 with preprocessing achieved the best performance (F1 score: 0.829). Models performed better with definitive cases than with initial ones, where performance dropped (F1 score: 0.705). Clinically, the model predicted full eruption or impaction with 83% and 75% accuracy, respectively, but showed lower accuracy for partial impactions. These results suggest that AI can support early prediction of third molar eruption status and enhance clinical decision-making. Deep learning models (particularly ResNet50) demonstrated promising results in predicting third molar eruption outcomes. With larger datasets and improved optimization, AI tools may achieve greater accuracy and support routine clinical applications. Full article
(This article belongs to the Special Issue Interpretable Deep Learning for Object Detection and Medical Image Classification)
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