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Diagnostics
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Advances in Medical Image Processing, Segmentation and Classification
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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: 31 January 2025 | Viewed by 32544

Special Issue Editors

Dr. Wan Azani Mustafa

E-Mail Website
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
Dr. Hiam Alquran

E-Mail Website
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

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Published Papers (10 papers)

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Research

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20 pages, 2429 KiB  
Article
Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images
by Yi-Ching Cheng, Yi-Chieh Hung, Guan-Hua Huang, Tai-Been Chen, Nan-Han Lu, Kuo-Ying Liu and Kuo-Hsuan Lin
Diagnostics 2024, 14(23), 2636; https://doi.org/10.3390/diagnostics14232636 - 22 Nov 2024
Abstract
Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based [...] Read more.
Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory and cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. In this paper, we present deep learning-based object detection methods for automatically identifying and annotating abnormal regions in CXR images. Methods: We developed and tested our models using disease-labeled CXR images and location-bounding boxes from E-Da Hospital. Given the prevalence of normal images over diseased ones in clinical settings, we created various training datasets and approaches to assess how different proportions of background images impact model performance. To address the issue of limited examples for certain diseases, we also investigated few-shot object detection techniques. We compared convolutional neural networks (CNNs) and Transformer-based models to determine the most effective architecture for medical image analysis. Results: The findings show that background image proportions greatly influenced model inference. Moreover, schemes incorporating binary classification consistently improved performance, and CNN-based models outperformed Transformer-based models across all scenarios. Conclusions: We have developed a more efficient and reliable system for the automated detection of disease labels and location bounding boxes in CXR images. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
12 pages, 8410 KiB  
Article
Enhancing Retina Images by Lowpass Filtering Using Binomial Filter
by Mofleh Hannuf AlRowaily, Hamzah Arof, Imanurfatiehah Ibrahim, Haniza Yazid and Wan Amirul Mahyiddin
Diagnostics 2024, 14(15), 1688; https://doi.org/10.3390/diagnostics14151688 - 5 Aug 2024
Viewed by 896
Abstract
This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue [...] Read more.
This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue channels are read and stored in separate arrays. Then, the area of the eye also called the region of interest (ROI) is located by thresholding. Next, the ratios of R to G and B to G at every pixel in the ROI are calculated and stored along with copies of the R, G and B channels. Then, the RGB channels are subjected to average filtering using a 3 × 3 mask to smoothen the RGB values of pixels, especially along the border of the ROI. In the background brightness estimation stage, the ROI of the three channels is filtered by binomial filters (BFs). This step creates a background brightness (BB) surface of the eye region by levelling the foreground objects like blood vessels, fundi, optic discs and blood spots, thus allowing the estimation of the background illumination. In the next stage, using the BB, the luminosity of the ROI is equalized so that all pixels will have the same background brightness. This is followed by a contrast adjustment of the ROI using CLAHE. Afterward, details of the adjusted green channel are enhanced using information from the adjusted red and blue channels. In the color correction stage, the intensities of pixels in the red and blue channels are adjusted according to their original ratios to the green channel before the three channels are reunited. The resulting color image resembles the original one in color distribution and tone but shows marked improvement in luminosity and contrast. The effectiveness of the approach is tested on the test images and enhancement is noticeable visually and quantitatively in greyscale and color. On average, this method manages to increase the contrast and luminosity of the images. The proposed method was implemented using MATLAB R2021b on an AMD 5900HS processor and the average execution time was less than 10 s. The performance of the filter is compared to those of two other filters and it shows better results. This technique can be a useful tool for ophthalmologists who perform diagnoses on the eyes of diabetic patients. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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10 pages, 2810 KiB  
Communication
Clinical Trial Validation of Automated Segmentation and Scoring of Pulmonary Cysts in Thoracic CT Scans
by Aneesha Baral, Simone Lee, Farah Hussaini, Brianna Matthew, Alfredo Lebron, Muyang Wang, Li-Yueh Hsu, Joel Moss and Han Wen
Diagnostics 2024, 14(14), 1529; https://doi.org/10.3390/diagnostics14141529 - 15 Jul 2024
Viewed by 804
Abstract
In cystic lung diseases such as lymphangioleiomyomatosis (LAM), a CT-based cyst score that measures the percentage of the lung volume occupied by cysts is a common index of the cyst burden in the lungs. Although the current semi-automatic measurement of the cyst score [...] Read more.
In cystic lung diseases such as lymphangioleiomyomatosis (LAM), a CT-based cyst score that measures the percentage of the lung volume occupied by cysts is a common index of the cyst burden in the lungs. Although the current semi-automatic measurement of the cyst score is well established, it is susceptible to human operator variabilities. We recently developed a fully automatic method incorporating adaptive features in place of manual adjustments. In this clinical study, the automatic method is validated against the standard method in several aspects. These include the agreement between the cyst scores of the two methods, the agreement of each method with independent tests of pulmonary function, and the temporal consistency of the measurements in the consecutive visits of the same patients. We found that the automatic method agreed with the standard method as well as the agreement between two trained operators running the same standard method; both methods obtained the same level of correlation with laboratory pulmonary function tests; the automated method had better temporal consistency than the standard method (p < 0.0001). The study indicates that the automatic method could replace the standard method and provide better consistency in assessing the extent of cystic changes in the lungs of patients. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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11 pages, 969 KiB  
Article
Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches
by Irene Ligato, Giorgio De Magistris, Emanuele Dilaghi, Giulio Cozza, Andrea Ciardiello, Francesco Panzuto, Stefano Giagu, Bruno Annibale, Christian Napoli and Gianluca Esposito
Diagnostics 2024, 14(13), 1376; https://doi.org/10.3390/diagnostics14131376 - 28 Jun 2024
Cited by 1 | Viewed by 1008
Abstract
Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using [...] Read more.
Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant’Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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14 pages, 3624 KiB  
Article
Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy–Paste
by Semin Kim, Huisu Yoon and Jongha Lee
Diagnostics 2024, 14(10), 1040; https://doi.org/10.3390/diagnostics14101040 - 17 May 2024
Viewed by 919
Abstract
Facial acne is a prevalent dermatological condition regularly observed in the general population. However, it is important to detect acne early as the condition can worsen if not treated. For this purpose, deep-learning-based methods have been proposed to automate detection, but acquiring acne [...] Read more.
Facial acne is a prevalent dermatological condition regularly observed in the general population. However, it is important to detect acne early as the condition can worsen if not treated. For this purpose, deep-learning-based methods have been proposed to automate detection, but acquiring acne training data is not easy. Therefore, this study proposes a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy–paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 in Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrated an improvement in performance, offering a novel direction for future acne analysis. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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18 pages, 3262 KiB  
Article
IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI
by Yuting Xie, Fulvio Zaccagna, Leonardo Rundo, Claudia Testa, Ruifeng Zhu, Caterina Tonon, Raffaele Lodi and David Neil Manners
Diagnostics 2024, 14(10), 997; https://doi.org/10.3390/diagnostics14100997 - 11 May 2024
Viewed by 1232
Abstract
Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious “black boxes”. The opaqueness of the model and the reasoning process make it difficult for health workers to [...] Read more.
Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious “black boxes”. The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes. Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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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
Cited by 1 | Viewed by 1597
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 9 | Viewed by 8560
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 6 | Viewed by 4650
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

Jump to: Research, Review

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 - 7 Oct 2023
Cited by 16 | Viewed by 11308
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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