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Deep Learning for Biomedical Image Analysis: Recent Advances and Future...
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Deep Learning for Biomedical Image Analysis: Recent Advances and Future Trends

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 884

Special Issue Editors

Dr. Madaín Pérez‐Patricio

E-Mail Website
Guest Editor
Tuxtla Gutiérrez Institute of Technology, Tuxtla Gutierrez 29020, Mexico
Interests: artificial intelligence; computer vision; specialized computing architectures for real-time image processing
Prof. Dr. Donato Cascio

E-Mail Website
Guest Editor
Department of Physics and Chemistry, University of Palermo, 90133 Palermo, Italy
Interests: medical imaging; artificial intelligence; pattern recognition; machine learning; applied physics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning has revolutionized biomedical image analysis, enabling significant advancements in disease diagnosis, treatment planning, and drug discovery. This Special Issue aims to showcase cutting-edge research and novel applications of deep learning techniques in biomedical imaging. We invite original research articles, comprehensive reviews, and insightful perspectives exploring the following topics:

  • Novel deep learning architectures: CNNs, RNNs, GANs, transformers, and hybrid models tailored for biomedical image analysis.
  • Applications including diverse imaging modalities, such as microscopy, endoscopy, radiology (X-ray, CT, MRI), ultrasound, and multimodal imaging.
  • Specific clinical challenges: Cancer detection, neurological disorders, cardiovascular diseases, ophthalmology, and infectious disease diagnosis.
  • Methodological advancements: Explainable AI, uncertainty quantification, federated learning, and data augmentation strategies for biomedical images.
  • Emerging trends: Integration of deep learning with other technologies, ethical considerations, and the development of robust and clinically translatable solutions.

This Special Issue will provide a platform for researchers to disseminate their latest findings and foster collaborations, ultimately contributing to advancing deep learning for improved healthcare outcomes.

Dr. Madaín Pérez‐Patricio
Prof. Dr. Donato Cascio
Guest Editors

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Keywords

  • artificial intelligence
  • computer vision
  • biomedical imaging
  • disease diagnosis

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Published Papers (1 paper)

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16 pages, 1300 KB  
Article
Multi-Class Segmentation and Classification of Intestinal Organoids: YOLO Stand-Alone vs. Hybrid Machine Learning Pipelines
by Luana Conte, Giorgio De Nunzio, Giuseppe Raso and Donato Cascio
Appl. Sci. 2025, 15(21), 11311; https://doi.org/10.3390/app152111311 - 22 Oct 2025
Viewed by 489
Abstract
Background: The automated analysis of intestinal organoids in microscopy images are essential for high-throughput morphological studies, enabling precision and scalability. Traditional manual analysis is time-consuming and subject to observer bias, whereas Machine Learning (ML) approaches have recently demonstrated superior performance. Purpose: [...] Read more.
Background: The automated analysis of intestinal organoids in microscopy images are essential for high-throughput morphological studies, enabling precision and scalability. Traditional manual analysis is time-consuming and subject to observer bias, whereas Machine Learning (ML) approaches have recently demonstrated superior performance. Purpose: This study aims to evaluate YOLO (You Only Look Once) for organoid segmentation and classification, comparing its standalone performance with a hybrid pipeline that integrates DL-based feature extraction and ML classifiers. Methods: The dataset, consisting of 840 light microscopy images and over 23,000 annotated intestinal organoids, was divided into training (756 images) and validation (84 images) sets. Organoids were categorized into four morphological classes: cystic non-budding organoids (Org0), early organoids (Org1), late organoids (Org3), and Spheroids (Sph). YOLO version 10 (YOLOv10) was trained as a segmenter-classifier for the detection and classification of organoids. Performance metrics for YOLOv10 as a standalone model included Average Precision (AP), mean AP at 50% overlap (mAP50), and confusion matrix evaluated on the validation set. In the hybrid pipeline, trained YOLOv10 segmented bounding boxes, and features extracted from these regions using YOLOv10 and ResNet50 were classified with ML algorithms, including Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Random Forest, eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptrons (MLP). The performance of these classifiers was assessed using the Receiver Operating Characteristic (ROC) curve and its corresponding Area Under the Curve (AUC), precision, F1 score, and confusion matrix metrics. Principal Component Analysis (PCA) was applied to reduce feature dimensionality while retaining 95% of cumulative variance. To optimize the classification results, an ensemble approach based on AUC-weighted probability fusion was implemented to combine predictions across classifiers. Results: YOLOv10 as a standalone model achieved an overall mAP50 of 0.845, with high AP across all four classes (range 0.797–0.901). In the hybrid pipeline, features extracted with ResNet50 outperformed those extracted with YOLO, with multiple classifiers achieving AUC scores ranging from 0.71 to 0.98 on the validation set. Among all classifiers, Logistic Regression emerged as the best-performing model, achieving the highest AUC scores across multiple classes (range 0.93–0.98). Feature selection using PCA did not improve classification performance. The AUC-weighted ensemble method further enhanced performance, leveraging the strengths of multiple classifiers to optimize prediction, as demonstrated by improved ROC-AUC scores across all organoid classes (range 0.92–0.98). Conclusions: This study demonstrates the effectiveness of YOLOv10 as a standalone model and the robustness of hybrid pipelines combining ResNet50 feature extraction and ML classifiers. Logistic Regression emerged as the best-performing classifier, achieving the highest ROC-AUC across multiple classes. This approach ensures reproducible, automated, and precise morphological analysis, with significant potential for high-throughput organoid studies and live imaging applications. Full article
(This article belongs to the Special Issue Deep Learning for Biomedical Image Analysis: Recent Advances and Future Trends)
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