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

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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: 31 December 2023 | Viewed by 6942

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Special Issue Editors

Dr. Wan Azani Mustafa

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Guest Editor

1 Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia 2 Advanced Computing (AdvComp), Centre of Excellence (CoE), Universiti Malaysia Perlis, 02600 Arau, 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

Prof. Dr. Kenji Suzuki

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Guest Editor

Artificial Intelligence in Biomedical Imaging Lab (AIBI Lab), Laboratory for Future Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo 152-8550, 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 2000 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 (6 papers)

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Research

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Open AccessArticle

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise

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

Viewed by 707

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, 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

(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Computer-Aided Diagnosis and Prognosis of Diseases”)

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Open AccessArticle

Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images

Thaqifah Ahmad Aris

Aimi Salihah Abdul Nasir

and

Diagnostics 2023, 13(3), 511; https://doi.org/10.3390/diagnostics13030511 - 31 Jan 2023

Viewed by 883

Abstract

Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites’ stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Computer-Aided Diagnosis and Prognosis of Diseases”)

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Open AccessArticle

Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning

and

Diagnostics 2022, 12(12), 3204; https://doi.org/10.3390/diagnostics12123204 - 17 Dec 2022

Viewed by 855

Abstract

A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Computer-Aided Diagnosis and Prognosis of Diseases”)

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Open AccessArticle

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Ahmad Ashraf Abdul Halim

Allan Melvin Andrew

Wan Azani Mustafa

Mohd Najib Mohd Yasin

Muzammil Jusoh

Vijayasarveswari Veeraperumal

Mohd Amiruddin Abd Rahman

Norshuhani Zamin

Mervin Retnadhas Mary

and

Sabira Khatun

Diagnostics 2022, 12(11), 2870; https://doi.org/10.3390/diagnostics12112870 - 19 Nov 2022

Cited by 1 | Viewed by 722

Abstract

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Computer-Aided Diagnosis and Prognosis of Diseases”)

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Open AccessArticle

Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach

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Diagnostics 2022, 12(11), 2756; https://doi.org/10.3390/diagnostics12112756 - 10 Nov 2022

Cited by 2 | Viewed by 1812

Abstract

The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors’ increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Computer-Aided Diagnosis and Prognosis of Diseases”)

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Review

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Open AccessReview

Pap Smear Images Classification Using Machine Learning: A Literature Matrix

and

Khairul Shakir Ab Rahman

Diagnostics 2022, 12(12), 2900; https://doi.org/10.3390/diagnostics12122900 - 22 Nov 2022

Cited by 2 | Viewed by 1235

Abstract

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Computer-Aided Diagnosis and Prognosis of Diseases”)

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