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Mathematics
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Deep Learning Methods for Biomedical and Medical Images
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Deep Learning Methods for Biomedical and Medical Images

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5937

Special Issue Editors

Dr. Jihye Bae

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: neural engineering; biomedical signal processing; medical image processing; brain–machine interfaces; reinforcement learning, epilepsy; EEG source imaging
Dr. Andres Alvarez-Meza

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Computer Engineering, Universidad Nacional de Colombia, Manizales 17001, Colombia
Interests: machine learning; deep leaerning; signal processing; neuro-engineering; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning has been an active topic in machine learning and has become the dominant approach in several domains, such as computer vision and natural language processing. In biomedical and medical image processing, machine learning paradigms, including supervised, self-supervised, unsupervised, and reinforcement learning, have been considered for various applications, such as image classification, segmentation, and detection. In supervised learning, convolutional neural networks are one of the most prevalent architectures to train labelled images, and have shown applicability in biomedical and medical image processing. Self-supervised, along with unsupervised learning, allows for the automatic discovery of important image features and assists in the interpretation of image characteristics. In addition, reinforcement learning has a unique approach based on indirect indication, called reward, and can contribute to image analysis and the optimization of hyperparameters including neural network architectures.

Although impressive results have been reported in biomedical and medical images, given the high stakes of this domain, there are several challenges that need to be addressed before these methods are widely adopted. Transfer learning is a well-known strategy in deep learning to overcome data scarcity, and its efficacy in medical image processing has been reported. However, most studies provide heuristic results without providing generalized rules for application in a specific application. Furthermore, the interpretability of deep learning algorithms can provide an in-depth explanation of their behavior. Nevertheless, despite its importance, the robustness of deep learning algorithms is still underexplored. Understanding these characteristics could help to expand the use of deep learning in biomedical and medical processing.

In this Special Issue, we welcome contributions that address these challenges and could lead to the wider adoption of deep learning in medical imaging.

Dr. Jihye Bae
Dr. Andres Alvarez-Meza
Guest Editors

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Keywords

  • supervised learning
  • self-supervised learning
  • unsupervised learning
  • reinforcement learning
  • transfer learning
  • interpretable models
  • robust methods

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Published Papers (4 papers)

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Research

24 pages, 7865 KiB  
Article
Context-Aware Level-Wise Feature Fusion Network with Anomaly Focus for Precise Classification of Incomplete Atypical Femoral Fractures in X-Ray Images
by Joonho Chang, Junwon Lee, Doyoung Kwon, Jin-Han Lee, Minho Lee, Sungmoon Jeong, Joon-Woo Kim, Heechul Jung and Chang-Wug Oh
Mathematics 2024, 12(22), 3613; https://doi.org/10.3390/math12223613 - 19 Nov 2024
Viewed by 781
Abstract
Incomplete Atypical Femoral Fracture (IAFF) is a precursor to Atypical Femoral Fracture (AFF). If untreated, it progresses to a complete fracture, increasing mortality risk. However, due to their small and ambiguous features, IAFFs are often misdiagnosed even by specialists. In this paper, we [...] Read more.
Incomplete Atypical Femoral Fracture (IAFF) is a precursor to Atypical Femoral Fracture (AFF). If untreated, it progresses to a complete fracture, increasing mortality risk. However, due to their small and ambiguous features, IAFFs are often misdiagnosed even by specialists. In this paper, we propose a novel approach for accurately classifying IAFFs in X-ray images across various radiographic views. We design a Dual Context-aware Complementary Extractor (DCCE) to capture both the overall femur characteristics and IAFF details with the surrounding context, minimizing information loss. We also develop a Level-wise Perspective-preserving Fusion Network (LPFN) that preserves the perspective of features while integrating them at different levels to enhance model representation and sensitivity by learning complex correlations and features that are difficult to obtain independently. Additionally, we incorporate the Spatial Anomaly Focus Enhancer (SAFE) to emphasize anomalous regions, preventing the model bias toward normal regions, and reducing False Negatives and missed IAFFs. Experimental results show significant improvements across all evaluation metrics, demonstrating high reliability in terms of accuracy (0.931), F1-score (0.9456), and AUROC (0.9692), proving the model’s potential for application in real medical settings. Full article
(This article belongs to the Special Issue Deep Learning Methods for Biomedical and Medical Images)
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33 pages, 13566 KiB  
Article
KOC_Net: Impact of the Synthetic Minority Over-Sampling Technique with Deep Learning Models for Classification of Knee Osteoarthritis Using Kellgren–Lawrence X-Ray Grade
by Syeda Nida Hassan, Mudassir Khalil, Humayun Salahuddin, Rizwan Ali Naqvi, Daesik Jeong and Seung-Won Lee
Mathematics 2024, 12(22), 3534; https://doi.org/10.3390/math12223534 - 12 Nov 2024
Cited by 1 | Viewed by 1099
Abstract
One of the most common diseases afflicting humans is knee osteoarthritis (KOA). KOA occurs when the knee joint cartilage breaks down, and knee bones start rubbing together. The diagnosis of KOA is a lengthy process, and missed diagnosis can have serious consequences. Therefore, [...] Read more.
One of the most common diseases afflicting humans is knee osteoarthritis (KOA). KOA occurs when the knee joint cartilage breaks down, and knee bones start rubbing together. The diagnosis of KOA is a lengthy process, and missed diagnosis can have serious consequences. Therefore, the diagnosis of KOA at an initial stage is crucial which prevents the patients from Severe complications. KOA identification using deep learning (DL) algorithms has gained popularity during the past few years. By applying knee X-ray images and the Kellgren–Lawrence (KL) grading system, the objective of this study was to develop a DL model for detecting KOA. This study proposes a novel model based on CNN called knee osteoarthritis classification network (KOC_Net). The KOC_Net model contains 05 convolutional blocks, and each convolutional block has three components such as Convlotuioanl2D, ReLU, and MaxPooling 2D. The KOC_Net model is evaluated on two publicly available benchmark datasets which consist of X-ray images of KOA based on the KL grading system. Additionally, we applied contrast-limited adaptive histogram equalization (CLAHE) methods to enhance the contrast of the images and utilized SMOTE Tomek to deal with the problem of minority classes. For the diagnosis of KOA, the classification performance of the proposed KOC_Net model is compared with baseline deep networks, namely Dense Net-169, Vgg-19, Xception, and Inception-V3. The proposed KOC_Net was able to classify KOA into 5 distinct groups (including Moderate, Minimal, Severe, Doubtful, and Healthy), with an AUC of 96.71%, accuracy of 96.51%, recall of 91.95%, precision of 90.25%, and F1-Score of 96.70%. Dense Net-169, Vgg-19, Xception, and Inception-V3 have relative accuracy rates of 84.97%, 81.08%, 87.06%, and 83.62%. As demonstrated by the results, the KOC_Net model provides great assistance to orthopedics in making diagnoses of KOA. Full article
(This article belongs to the Special Issue Deep Learning Methods for Biomedical and Medical Images)
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19 pages, 22638 KiB  
Article
Fast Semi-Supervised t-SNE for Transfer Function Enhancement in Direct Volume Rendering-Based Medical Image Visualization
by Walter Serna-Serna, Andrés Marino Álvarez-Meza and Álvaro Orozco-Gutiérrez
Mathematics 2024, 12(12), 1885; https://doi.org/10.3390/math12121885 - 17 Jun 2024
Viewed by 1410
Abstract
Magnetic resonance imaging and computed tomography produce three-dimensional volumetric medical images. While a scalar value represents each individual volume element, or voxel, volumetric data are characterized by features derived from groups of neighboring voxels and their inherent relationships, which may vary depending on [...] Read more.
Magnetic resonance imaging and computed tomography produce three-dimensional volumetric medical images. While a scalar value represents each individual volume element, or voxel, volumetric data are characterized by features derived from groups of neighboring voxels and their inherent relationships, which may vary depending on the specific clinical application. Labeled samples are also required in most applications, which can be problematic for large datasets such as medical images. We propose a direct volume rendering (DVR) framework based on multi-scale dimensionality reduction neighbor embedding that generates two-dimensional transfer function (TF) domains. In this way, we present FSS.t-SNE, a fast semi-supervised version of the t-distributed stochastic neighbor embedding (t-SNE) method that works over hundreds of thousands of voxels without the problem of crowding and with better separation in a 2D histogram compared to traditional TF domains. Our FSS.t-SNE scatters voxels of the same sub-volume in a wider region through multi-scale neighbor embedding, better preserving both local and global data structures and allowing for its internal exploration based on the original features of the multi-dimensional space, taking advantage of the partially provided labels. Furthermore, FSS.t-SNE untangles sample paths among sub-volumes, allowing us to explore edges and transitions. In addition, our approach employs a Barnes–Hut approximation to reduce computational complexity from O(N2) (t-SNE) to O(NlogN). Although we require the additional step of generating the 2D TF domain from multiple features, our experiments show promising performance in volume segmentation and visual inspection. Full article
(This article belongs to the Special Issue Deep Learning Methods for Biomedical and Medical Images)
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16 pages, 3879 KiB  
Article
Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image Classification
by Tamás Katona, Gábor Tóth, Mátyás Petró and Balázs Harangi
Mathematics 2024, 12(6), 806; https://doi.org/10.3390/math12060806 - 8 Mar 2024
Cited by 4 | Viewed by 1996
Abstract
Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional neural networks (CNNs) such as VGG16, [...] Read more.
Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional neural networks (CNNs) such as VGG16, ResNet 50, and DenseNet 121, modifying their output layers and fine-tuning the models. We employ a novel optimization strategy using the Hyperband algorithm to efficiently search the hyperparameter space while adjusting the fully connected layers of the CNNs. The effectiveness of our approach is evaluated on the basis of the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metric. Our proposed methodology could assist in automated chest radiograph interpretation, offering a valuable tool that can be used by clinicians in the future. Full article
(This article belongs to the Special Issue Deep Learning Methods for Biomedical and Medical Images)
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