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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 for Multidisciplinary Applications".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 1436

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 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

Published Papers (2 papers)

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Research

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
Viewed by 413
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 617
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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