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Algorithms
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Algorithms for Computer Aided Diagnosis
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Algorithms for Computer Aided Diagnosis

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 9838

Special Issue 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 towards more efficient and precise healthcare solutions. Within the realm of medical technology, a diverse array of instruments comes into play, including temperature probes, heart rate monitors, and respiration rate counters. Yet, it is the 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 integration of artificial intelligence techniques, the diagnostic process has been revolutionized, streamlining a once time-consuming and cumbersome endeavor.

In this Special Issue, we delve deep into 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, drive the diagnosis of a myriad of diseases and disorders, utilizing data sourced from various medical instruments. As we strive towards a future marked by comprehensive and automated computer-aided diagnosis, the spotlight shines on these specialized machine learning algorithms. This journey transcends the confines of conventional practices, paving the way for innovative applications within the medical field. With each passing day, algorithms continue to reshape healthcare, propelling us towards a future in which precision and efficiency 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
  • Developing computer-aided diagnosis systems
  • Machine learning algorithms for medical images
  • Artificial intelligence algorithms in health care
  • 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 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. 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 1600 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 Issue

Published Papers (5 papers)

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Research

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23 pages, 1378 KiB  
Article
Optimizing Automated Brain Extraction for Moderate to Severe Traumatic Brain Injury Patients: The Role of Intensity Normalization and Bias-Field Correction
by Patrick Carbone, Celina Alba, Alexis Bennett, Kseniia Kriukova and Dominique Duncan
Algorithms 2024, 17(7), 281; https://doi.org/10.3390/a17070281 - 27 Jun 2024
Viewed by 1450
Abstract
Accurate brain extraction is crucial for the validity of MRI analyses, particularly in the context of traumatic brain injury (TBI), where conventional automated methods frequently fall short. This study investigates the interplay between intensity normalization, bias-field correction (also called intensity inhomogeneity correction), and [...] Read more.
Accurate brain extraction is crucial for the validity of MRI analyses, particularly in the context of traumatic brain injury (TBI), where conventional automated methods frequently fall short. This study investigates the interplay between intensity normalization, bias-field correction (also called intensity inhomogeneity correction), and automated brain extraction in MRIs of individuals with TBI. We analyzed 125 T1-weighted Magnetization-Prepared Rapid Gradient-Echo (T1-MPRAGE) and 72 T2-weighted Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI sequences from a cohort of 143 patients with moderate to severe TBI. Our study combined 14 different intensity processing procedures, each using a configuration of N3 inhomogeneity correction, Z-score normalization, KDE-based normalization, or WhiteStripe intensity normalization, with 10 different configurations of the Brain Extraction Tool (BET) and the Optimized Brain Extraction Tool (optiBET). Our results demonstrate that optiBET with N3 inhomogeneity correction produces the most accurate brain extractions, specifically with one iteration of N3 for T1-MPRAGE and four iterations for T2-FLAIR, and pipelines incorporating N3 inhomogeneity correction significantly improved the accuracy of BET as well. Conversely, intensity normalization demonstrated a complex relationship with brain extraction, with effects varying by the normalization algorithm and BET parameter configuration combination. This study elucidates the interactions between intensity processing and the accuracy of brain extraction. Understanding these relationships is essential to the effective and efficient preprocessing of TBI MRI data, laying the groundwork for the development of robust preprocessing pipelines optimized for multi-site TBI MRI data. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis)
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17 pages, 39975 KiB  
Article
A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
by Jose Dixon, Oluwatunmise Akinniyi, Abeer Abdelhamid, Gehad A. Saleh, Md Mahmudur Rahman and Fahmi Khalifa
Algorithms 2024, 17(6), 221; https://doi.org/10.3390/a17060221 - 21 May 2024
Cited by 2 | Viewed by 2102
Abstract
The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture [...] Read more.
The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture for improved brain tumor classification. We introduce a hybrid architecture that integrates vision transformer (ViT) and deep neural networks to create an ensemble classifier, resulting in a more robust brain tumor classification framework. The analysis pipeline begins with preprocessing and data normalization, followed by extracting three types of MRI-derived information-rich features. The latter included higher-order texture and structural feature sets to harness the spatial interactions between image intensities, which were derived using Haralick features and local binary patterns. Additionally, local deeper features of the brain images are extracted using an optimized convolutional neural networks (CNN) architecture. Finally, ViT-derived features are also integrated due to their ability to handle dependencies across larger distances while being less sensitive to data augmentation. The extracted features are then weighted, fused, and fed to a machine learning classifier for the final classification of brain MRIs. The proposed weighted ensemble architecture has been evaluated on publicly available and locally collected brain MRIs of four classes using various metrics. The results showed that leveraging the benefits of individual components of the proposed architecture leads to improved performance using ablation studies. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis)
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16 pages, 3323 KiB  
Article
Advancing Pulmonary Nodule Diagnosis by Integrating Engineered and Deep Features Extracted from CT Scans
by Wiem Safta and Ahmed Shaffie
Algorithms 2024, 17(4), 161; https://doi.org/10.3390/a17040161 - 18 Apr 2024
Cited by 3 | Viewed by 2477
Abstract
Enhancing lung cancer diagnosis requires precise early detection methods. This study introduces an automated diagnostic system leveraging computed tomography (CT) scans for early lung cancer identification. The main approach is the integration of three distinct feature analyses: the novel 3D-Local Octal Pattern (LOP) [...] Read more.
Enhancing lung cancer diagnosis requires precise early detection methods. This study introduces an automated diagnostic system leveraging computed tomography (CT) scans for early lung cancer identification. The main approach is the integration of three distinct feature analyses: the novel 3D-Local Octal Pattern (LOP) descriptor for texture analysis, the 3D-Convolutional Neural Network (CNN) for extracting deep features, and geometric feature analysis to characterize pulmonary nodules. The 3D-LOP method innovatively captures nodule texture by analyzing the orientation and magnitude of voxel relationships, enabling the distinction of discriminative features. Simultaneously, the 3D-CNN extracts deep features from raw CT scans, providing comprehensive insights into nodule characteristics. Geometric features and assessing nodule shape further augment this analysis, offering a holistic view of potential malignancies. By amalgamating these analyses, our system employs a probability-based linear classifier to deliver a final diagnostic output. Validated on 822 Lung Image Database Consortium (LIDC) cases, the system’s performance was exceptional, with measures of 97.84%, 98.11%, 94.73%, and 0.9912 for accuracy, sensitivity, specificity, and Area Under the ROC Curve (AUC), respectively. These results highlight the system’s potential as a significant advancement in clinical diagnostics, offering a reliable, non-invasive tool for lung cancer detection that promises to improve patient outcomes through early diagnosis. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis)
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13 pages, 1103 KiB  
Article
On the Need for Accurate Brushstroke Segmentation of Tablet-Acquired Kinematic and Pressure Data: The Case of Unconstrained Tracing
by Karly S. Franz, Grace Reszetnik and Tom Chau
Algorithms 2024, 17(3), 128; https://doi.org/10.3390/a17030128 - 20 Mar 2024
Viewed by 1181
Abstract
Brushstroke segmentation algorithms are critical in computer-based analysis of fine motor control via handwriting, drawing, or tracing tasks. Current segmentation approaches typically rely only on one type of feature, either spatial, temporal, kinematic, or pressure. We introduce a segmentation algorithm that leverages both [...] Read more.
Brushstroke segmentation algorithms are critical in computer-based analysis of fine motor control via handwriting, drawing, or tracing tasks. Current segmentation approaches typically rely only on one type of feature, either spatial, temporal, kinematic, or pressure. We introduce a segmentation algorithm that leverages both spatiotemporal and pressure features to accurately identify brushstrokes during a tracing task. The algorithm was tested on both a clinical and validation dataset. Using validation trials with incorrectly identified brushstrokes, we evaluated the impact of segmentation errors on commonly derived biomechanical features used in the literature to detect graphomotor pathologies. The algorithm exhibited robust performance on validation and clinical datasets, effectively identifying brushstrokes while simultaneously eliminating spurious, noisy data. Spatial and temporal features were most affected by incorrect segmentation, particularly those related to the distance between brushstrokes and in-air time, which experienced propagated errors of 99% and 95%, respectively. In contrast, kinematic features, such as velocity and acceleration, were minimally affected, with propagated errors between 0 to 12%. The proposed algorithm may help improve brushstroke segmentation in future studies of handwriting, drawing, or tracing tasks. Spatial and temporal features derived from tablet-acquired data should be considered with caution, given their sensitivity to segmentation errors and instrumentation characteristics. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis)
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Review

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16 pages, 1055 KiB  
Review
Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
by Rodrigo Martins, Fátima Rodrigues, Susana Costa and Nelson Costa
Algorithms 2024, 17(6), 223; https://doi.org/10.3390/a17060223 - 23 May 2024
Viewed by 1685
Abstract
Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing [...] Read more.
Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing the PSALSAR framework, incorporating the PICOC method and PRISMA statement for comprehensive research, 22 publications were scrutinized from the Scopus, Web of Science, and PubMed databases. A diverse range of sensor fusion methods, data signal analysis techniques, and classifier performances were investigated. Notably, Madgwick’s algorithm and the Principal Component Analysis showed superior performance in tracking respiratory movements. Classifiers like Long Short-Term Memory Recurrent Neural Networks exhibited high accuracy in detecting breathing events. Motion artifacts, limited sample sizes, and physiological variability posed challenges, highlighting the need for further research. Optimal sensor configurations were explored, suggesting improvements with multiple sensors, especially in different body postures. In conclusion, this systematic literature review elucidates methods, challenges, and potential future developments in using inertial sensors for assessing adult human breathing patterns. Overcoming the challenges related to sensor placement, motion artifacts, and algorithm development is essential for progress. Future research should focus on extending sensor applications to clinical settings and diverse populations, enhancing respiratory health management. Full article
(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis)
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