Editorial Board Members’ Collection Series in “Computer-Aided Diagnosis and Prognosis of Diseases”

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4450

Special Issue Editors


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Guest Editor
1. Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
2. Advanced Computing (AdvComp), Centre of Excellence (CoE), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
Interests: biomedical imaging; image processing; digital signal processing; artificial intelligence; feature extraction; recognition and classification
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Biomedical Artificial Intelligence Research Unit (BMAI), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
Interests: machine learning; deep learning; artificial intelligence; medical image analysis; medical imaging; computer-aided diagnosis; signal and image processing; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

While traditional diagnosis remains a key component of clinical workup, many diseases can benefit from earlier and more refined characterization to make the most of increasingly advanced, often personalized, therapies and multimodality treatment regimens. In the modern multidisciplinary clinical context, such advances tend to rely on the collation and analysis of varied information and data, obtained at various stages of a patient's workup. The extraction, quantity and variety of such data and their subsequent synthesis is often well beyond human capabilities, and thus requires the use of advanced computed analysis. This is from where the concept of Computer-Aided Diagnosis and related approaches arose, and while these approaches were initially developed for extracting and combining features derived from images (e.g., from modalities such as X-ray, CT, mammography, MR, US, PET and SPECT), they have subsequently and more recently been broadened to encompass all types of clinical data and biomarkers (from genomics to imaging, alongside etiology, environment, etc.), with the aim of providing an actionable understanding of disease to assist in its detailed characterization (e.g., type, stage) and optimize interventional strategies. This Special Issue will gather as comprehensive a collection of relevant examples of such approaches as possible, from any stage of the clinical workup, across specialties, disciplines and applications.

Dr. Wan Azani Mustafa
Prof. Dr. Kenji Suzuki
Guest Editors

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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • computer-aided detection and diagnosis
  • early detection, diagnosis, characterization and intervention
  • advanced imaging
  • quantitative imaging
  • image-guided therapy
  • personalized medicine
  • precision medicine
  • biomarkers
  • machine/deep learning
  • radiogenomics
  • radiomics
  • computational/artificial intelligence
  • prognosis

Published Papers (2 papers)

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Research

34 pages, 9065 KiB  
Article
Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm
by Jaskaran Singh, Narpinder Singh, Mostafa M. Fouda, Luca Saba and Jasjit S. Suri
Diagnostics 2023, 13(12), 2092; https://doi.org/10.3390/diagnostics13122092 - 16 Jun 2023
Cited by 5 | Viewed by 1333
Abstract
Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in [...] Read more.
Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures are superior compared to attention-not-enabled SDL (aneSDL) or aeSDL models. We designed EDL-based architectures with attention blocks to build eleven kinds of SDL model and five kinds of EDL model on four domain-specific datasets. We scientifically validated our models by comparing “seen” and “unseen” paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). For the benchmarking dataset (SemEval), the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings. Full article
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22 pages, 2969 KiB  
Article
Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise
by Jayakumar Kaliappan, Apoorva Reddy Bagepalli, Shubh Almal, Rishabh Mishra, Yuh-Chung Hu and Kathiravan Srinivasan
Diagnostics 2023, 13(10), 1692; https://doi.org/10.3390/diagnostics13101692 - 10 May 2023
Cited by 2 | Viewed by 2090
Abstract
Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, [...] Read more.
Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values. Full article
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