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Algorithms
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Algorithms for Computer Aided Diagnosis: 3rd Edition
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Algorithms for Computer Aided Diagnosis: 3rd Edition

A Special Issue of Algorithms (ISSN 1999-4893) belonging to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1847

Editor

Dr. Ahmed Shaffie

E-Mail Website
Guest Editor
Mathematics and Computer Science Department, College of Natural Sciences and Mathematics, Louisiana State University of Alexandria, Alexandria, LA 71302, USA
Interests: medical Imaging; non-invasive computer-assisted diagnosis systems; image and video processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Algorithms stand at the forefront of modern medical diagnostics, catalyzing a paradigm shift away from conventional methods toward more efficient and precise healthcare solutions. Within the realm of medical technology, a diverse array of instruments come into play, including temperature probes, heart rate monitors, and respiration rate counters. However, it is algorithms that serve as the linchpin of this transformation. These computational powerhouses breathe life into these devices, interpreting complex physiological data with unprecedented accuracy. For instance, electrocardiogram readings capture the heart's electrical activity, while respiration rate data count chest movements per minute. Through the seamless incorporation of artificial intelligence techniques, the diagnostic process has been revolutionized, streamlining a once time-consuming and cumbersome endeavor.

In this Special Issue, we will closely examine the cutting-edge applications of AI in medical diagnostics, showcasing state-of-the-art approaches that promise to reshape the healthcare landscape. These algorithms, finely tuned for this purpose, are driving diagnoses in a myriad of diseases and disorders, utilizing data sourced from various medical instruments. As we are striving toward a future marked by comprehensive and automated computer-aided diagnosis, it is important to provide insight into these specialized machine learning algorithms. This is an inter-disciplinary endeavor that will pave the way for innovative applications within the medical field. Algorithms are continuing to reshape healthcare, propelling us toward a future in which precision and efficiency will define the standard of medical practice, ultimately leading to improved patient outcomes.

The scope of this Special Issue includes, but is not limited to, the following:

  • Innovative technological advancements in the medical field;
  • Development of computer-aided diagnosis systems;
  • Machine learning algorithms for medical images;
  • Artificial intelligence algorithms in healthcare;
  • Algorithm-driven wearable devices for comprehensive health assessment;
  • Enhanced medical image analysis with machine learning algorithms.

Dr. Ahmed Shaffie
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 1800 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

  • algorithms
  • machine learning
  • artificial intelligence (AI)
  • computer-aided diagnosis (CAD)
  • healthcare revolution
  • medical devices

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Related Special Issues

Published Papers (4 papers)

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Research

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41 pages, 20383 KB  
Article
A Hybrid Gabor–ViT Embedding with Gated Mamba Deep Learning Framework for Brain Tumor MRI Classification
by Baidaa Mutasher Rashed, Zahoor M. Aydam and Nidhal Khdhair El Abbadi
Algorithms 2026, 19(9), 790; https://doi.org/10.3390/a19090790 - 15 Sep 2026
Abstract
Brain tumor classification from MRI is an important task in medical image analysis. This study introduces a new hybrid deep learning method that integrates Gabor filtering for the improvement in texture feature extraction, the Vision Transformer (ViT) framework for extraction of the global [...] Read more.
Brain tumor classification from MRI is an important task in medical image analysis. This study introduces a new hybrid deep learning method that integrates Gabor filtering for the improvement in texture feature extraction, the Vision Transformer (ViT) framework for extraction of the global context dependency and fine-grained spatial features, and a novel Gated Mamba to enhance feature classification efficiency. The framework was first evaluated in-distribution on the Kaggle dataset, which contains 5712 slices of brain MRI images from four different classes using accuracy, precision, recall, F1-score, AUC, Std, and 95% confidence intervals (CIs), with a mean image-level accuracy of 99.62%. Because the Kaggle dataset is known to be near-saturated and no patient identifiers are available, this in-distribution result is treated as a sanity check rather than a measure of clinical generalization; pHash deduplication was used to remove visually identical images, but this does not provide patient-level grouping, and residual patient-level leakage cannot be fully excluded. The central evaluation of this study is therefore a cross-dataset validation on the “figshare brain mri” dataset, which uses a different scanner and acquisition protocol and was never used during training and model selection; the proposed model on this dataset achieves 87.23% accuracy with reasonable confidence intervals, a decline of 12.39 percentage points relative to the in-distribution result that reflects the domain-shift gap typical of this task. The proposed hybrid approach performs competitively against established methods in-distribution, while the cross-dataset results highlight generalization challenges that motivate the failure analysis and limitations discussed in this paper. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 3rd Edition)
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33 pages, 20134 KB  
Article
Explainable Deep Learning for Computer-Aided Skin Cancer Detection Using CNNs and Vision Transformers
by Eirini Karantina, Antreas Kantaros, Grigoris Nikolaou, Nikolaos Laskaris and Paraskevi Zacharia
Algorithms 2026, 19(8), 638; https://doi.org/10.3390/a19080638 - 1 Aug 2026
Viewed by 327
Abstract
Early and accurate detection of skin cancer, particularly melanoma, remains a critical challenge in computer-aided diagnosis, motivating the development of reliable and interpretable machine learning solutions. This study presents a comparative algorithmic analysis of deep learning models for automated skin cancer detection using [...] Read more.
Early and accurate detection of skin cancer, particularly melanoma, remains a critical challenge in computer-aided diagnosis, motivating the development of reliable and interpretable machine learning solutions. This study presents a comparative algorithmic analysis of deep learning models for automated skin cancer detection using dermoscopic images. Specifically, convolutional neural networks (CNNs) and Vision Transformers (ViTs) are implemented within a unified framework, employing transfer learning and standardized preprocessing techniques on a benchmark dataset. The proposed methodology incorporates data augmentation and class imbalance handling strategies, while model performance is evaluated using clinically relevant metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve. In addition, explainability techniques such as Grad-CAM and attention visualization are employed to enhance model interpretability, and decision threshold analysis is conducted to assess trade-offs between sensitivity and specificity in melanoma detection. Experimental results demonstrate that CNN-based architectures achieve robust performance in capturing local spatial features, while transformer-based models provide competitive results through global contextual representation. However, variations are observed in model calibration and false-negative rates, which are critical for clinical deployment. Overall, the findings highlight the importance of combining algorithmic performance with interpretability and threshold optimization to support reliable and clinically meaningful computer-aided diagnosis systems. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 3rd Edition)
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17 pages, 771 KB  
Article
MSA-Net: A Deep Learning Network with Multi-Axial Hadamard Attention and Pyramid Pooling for Stroke Microwave Imaging
by Bo Han, Dongliang Li, Xuhui Zhu, Mingshuai Zhang and Peng Li
Algorithms 2026, 19(4), 276; https://doi.org/10.3390/a19040276 - 2 Apr 2026
Viewed by 610
Abstract
Microwave imaging is emerging as an alternative to conventional medical diagnostic techniques. Traditional analytical and numerical methods fail to adequately address these fundamental challenges: they often rely on strict linear approximations or simplified physical models, leading to low reconstruction accuracy, poor robustness, and [...] Read more.
Microwave imaging is emerging as an alternative to conventional medical diagnostic techniques. Traditional analytical and numerical methods fail to adequately address these fundamental challenges: they often rely on strict linear approximations or simplified physical models, leading to low reconstruction accuracy, poor robustness, and limited generalization ability in complex clinical scenarios. As a result, they cannot meet the high-precision requirements of practical stroke microwave imaging. To further improve the accuracy of microwave imaging algorithms in recognizing stroke regions and solving the backscattering problem, this study employs a combination of methods with deep learning. It presents the Multi-Scale Attention Network (MSA-Net) for microwave imaging. The network is based on the EGE-UNet network structure with improved multi-axis Hadamard attention, incorporating null-space pyramid pooling and introducing a deep supervisory mechanism to improve the network performance further. To combine microwave imaging with deep learning, firstly, a large amount of microwave data need to be simulated with HFSS, in which the simulation model is a human brain stroke model constructed by an HFSS simulation system. Secondly, the microwave data obtained from the simulation are converted into a tensor format. Then, the tensor data are input into the MSA-Net neural network, which generates a binary mask image that can be used to detect the size and location of the stroke. This study also prompts the model to converge faster by sparsifying the microwave data to improve training efficiency. The method has been tested using simulation data, and based on the comparison experiments with other networks, MSA-Net is more accurate in detecting the location and the bleed size. The experimental results show that the proposed method is superior for stroke imaging. The experimental results show that the proposed model achieves a 1.08 improvement in peak signal-to-noise ratio and a 0.017 reduction in learned perceptual image block similarity, fully validating the effectiveness of the structural optimization strategy proposed in this paper. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 3rd Edition)
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Review

Jump to: Research

35 pages, 1343 KB  
Review
Trustworthy Gait Analysis for Computer-Aided Diagnosis in Parkinson’s Disease and Knee Osteoarthritis: A Targeted Narrative Review of Algorithms and Clinical Validation
by Jihoon Moon
Algorithms 2026, 19(8), 664; https://doi.org/10.3390/a19080664 - 10 Aug 2026
Viewed by 376
Abstract
Gait analysis is increasingly used as a dynamic functional biomarker for computer-aided diagnosis (CADx), although strong internal performance alone does not establish clinical utility. This targeted narrative review examines Parkinson’s disease (PD) and knee osteoarthritis (KOA) as its primary clinical contexts while treating [...] Read more.
Gait analysis is increasingly used as a dynamic functional biomarker for computer-aided diagnosis (CADx), although strong internal performance alone does not establish clinical utility. This targeted narrative review examines Parkinson’s disease (PD) and knee osteoarthritis (KOA) as its primary clinical contexts while treating fall risk and other mobility disorders as contextual extensions. A structured literature search and source-verification process covered studies available through 31 July 2026. The review corpus comprised 118 sources spanning clinical evidence, measurement validation, datasets, algorithmic architectures, and methodological guidance. This review critically compares sensing modalities, public and proprietary datasets, feature-based models, CNN/RNN architectures, graph neural networks, Transformers, state-space models, and trust-supporting approaches, including explainable artificial intelligence, automated machine learning, federated learning, and multimodal fusion. Using an explicit coverage rule, a common validation audit was applied to 15 empirical or measurement-validation studies. The audited evidence did not demonstrate mature independent multisite validation for disease-focused gait CADx. Formal probability calibration and quantitative testing of explanation stability were also absent, while publicly available KOA-specific multimodal benchmarks remained scarce. Based on these findings, this review proposes a six-level validation-readiness ladder in which independent external evidence at Level 3 represents the minimum threshold for initiating a supervised clinical pilot. The framework prioritizes subject-level separation, leakage control, calibration, clinically meaningful reference standards, and prospective workflow evaluation. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 3rd Edition)
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