Advances in Medical Image Processing, Segmentation and Classification

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: 30 April 2024 | Viewed by 14133

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
Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
Interests: image processing; digital signal processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical data contain information on a person's state of health and the medical treatment that they have received such as signals, images, sounds, chemical components and their concentration, body temperature, respiratory rate, blood pressure, and different treatment measurements to quantify the patient’s status and the disease stage. Nowadays, a computer-aided diagnosis (CAD) system involves various stages such as detection, segmentation, and classification. Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image-processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. Medical experts rely on the medical imaging modalities such as computed tomography (CT), microscopic blood smear images, magnetic resonance imaging (MRI), X-ray, and ultrasound (US) to diagnose health challenges and assign treatment prescriptions. Researchers and developers are able to deliver smart solutions for medical imaging diagnoses thanks to the AI-based potential functionalities of machine learning and deep learning technologies. Employing technological tools for collection, processing, and analysis will incorporate understanding the patient’s status and developing the treatment plan. Achieving highly accurate models needs huge datasets; this issue can be solved by having enough knowledge of medical data processing and its analysis.

In this Special Issue, “Advances in Medical Image Processing, Segmentation and Classification”, we will cover original articles, short communication, and reviews related to various computer-aided diagnosis methods for biomedical systems. Applications such as patient monitoring, disease diagnosis and progression, patient rehabilitation, and medical image analysis are encouraged. It is expected that you clearly indicate the novel aspects of signal processing or modelling that assisted you in solving your problem.

Dr. Wan Azani Mustafa
Dr. Hiam Alquran
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

  • medical image/bio-signal analysis
  • medical image segmentation/detection
  • healthcare systems
  • AI-based medical image registration
  • medical image recognition
  • biomedical systems
  • diagnostic aid
  • AI-based screening system
  • medical image
  • signal classification
  • biomedical image retrieval
  • medical image annotation
  • biomedical image summarization/filtering
  • cancer diagnosis
  • machine learning
  • deep learning
  • artificial intelligence
  • AI-based medical image diagnosis
  • medical deep learning CAD systems
  • XAI-based medical imaging
  • patient/treatment stratification based on AI image processing
  • synthetic medical image generation
  • explainable AI in medicine

Published Papers (4 papers)

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Research

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13 pages, 2551 KiB  
Article
Three-Dimensional Measurement of the Uterus on Magnetic Resonance Images: Development and Performance Analysis of an Automated Deep-Learning Tool
by Daphné Mulliez, Edouard Poncelet, Laurie Ferret, Christine Hoeffel, Blandine Hamet, Lan Anh Dang, Nicolas Laurent and Guillaume Ramette
Diagnostics 2023, 13(16), 2662; https://doi.org/10.3390/diagnostics13162662 - 12 Aug 2023
Viewed by 935
Abstract
Uterus measurements are useful for assessing both the treatment and follow-ups of gynaecological patients. The aim of our study was to develop a deep learning (DL) tool for fully automated measurement of the three-dimensional size of the uterus on magnetic resonance imaging (MRI). [...] Read more.
Uterus measurements are useful for assessing both the treatment and follow-ups of gynaecological patients. The aim of our study was to develop a deep learning (DL) tool for fully automated measurement of the three-dimensional size of the uterus on magnetic resonance imaging (MRI). In this single-centre retrospective study, 900 cases were included to train, validate, and test a VGG-16/VGG-11 convolutional neural network (CNN). The ground truth was manual measurement. The performance of the model was evaluated using the objective key point similarity (OKS), the mean difference in millimetres, and coefficient of determination R2. The OKS of our model was 0.92 (validation) and 0.96 (test). The average deviation and R2 coefficient between the AI measurements and the manual ones were, respectively, 3.9 mm and 0.93 for two-point length, 3.7 mm and 0.94 for three-point length, 2.6 mm and 0.93 for width, 4.2 mm and 0.75 for thickness. The inter-radiologist variability was 1.4 mm. A three-dimensional automated measurement was obtained in 1.6 s. In conclusion, our model was able to locate the uterus on MRIs and place measurement points on it to obtain its three-dimensional measurement with a very good correlation compared to manual measurements. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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Review

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32 pages, 6039 KiB  
Review
A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification
by Reham Kaifi
Diagnostics 2023, 13(18), 3007; https://doi.org/10.3390/diagnostics13183007 - 20 Sep 2023
Cited by 2 | Viewed by 3506
Abstract
Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; [...] Read more.
Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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25 pages, 1452 KiB  
Review
Cervical Cancer Detection Techniques: A Chronological Review
by Wan Azani Mustafa, Shahrina Ismail, Fahirah Syaliza Mokhtar, Hiam Alquran and Yazan Al-Issa
Diagnostics 2023, 13(10), 1763; https://doi.org/10.3390/diagnostics13101763 - 17 May 2023
Cited by 2 | Viewed by 2916
Abstract
Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological [...] Read more.
Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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Other

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40 pages, 492 KiB  
Systematic Review
Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review
by Taye Girma Debelee
Diagnostics 2023, 13(19), 3147; https://doi.org/10.3390/diagnostics13193147 - 07 Oct 2023
Cited by 3 | Viewed by 5960
Abstract
Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods [...] Read more.
Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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