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Suleiman TA, Anyimadu DT, Permana AD, Ngim HAA, Scotto di Freca A. Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm. Vis Comput Ind Biomed Art 2024; 7:15. [PMID: 38884841 PMCID: PMC11183002 DOI: 10.1186/s42492-024-00166-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/24/2024] [Indexed: 06/18/2024] Open
Abstract
Skin lesion classification plays a crucial role in the early detection and diagnosis of various skin conditions. Recent advances in computer-aided diagnostic techniques have been instrumental in timely intervention, thereby improving patient outcomes, particularly in rural communities lacking specialized expertise. Despite the widespread adoption of convolutional neural networks (CNNs) in skin disease detection, their effectiveness has been hindered by the limited size and data imbalance of publicly accessible skin lesion datasets. In this context, a two-step hierarchical binary classification approach is proposed utilizing hybrid machine and deep learning (DL) techniques. Experiments conducted on the International Skin Imaging Collaboration (ISIC 2017) dataset demonstrate the effectiveness of the hierarchical approach in handling large class imbalances. Specifically, employing DenseNet121 (DNET) as a feature extractor and random forest (RF) as a classifier yielded the most promising results, achieving a balanced multiclass accuracy (BMA) of 91.07% compared to the pure deep-learning model (end-to-end DNET) with a BMA of 88.66%. The RF ensemble exhibited significantly greater efficiency than other machine-learning classifiers in aiding DL to address the challenge of learning with limited data. Furthermore, the implemented predictive hybrid hierarchical model demonstrated enhanced performance while significantly reducing computational time, indicating its potential efficiency in real-world applications for the classification of skin lesions.
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Affiliation(s)
- Taofik Ahmed Suleiman
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Daniel Tweneboah Anyimadu
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Andrew Dwi Permana
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Hsham Abdalgny Abdalwhab Ngim
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Alessandra Scotto di Freca
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy.
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Lou J, Xiang Z, Fan Y, Song J, Huang N, Li J, Jin G, Cui S. The efficacy and safety of autologous epidermal cell suspensions for re-epithelialization of skin lesions: A systematic review and meta-analysis of randomized trials. Skin Res Technol 2024; 30:e13820. [PMID: 38898373 PMCID: PMC11186709 DOI: 10.1111/srt.13820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND Successful usage of autologous skin cell suspension (ASCS) has been demonstrated in some clinical trials. However, its efficacy and safety have not been verified. This latest systematic review and meta-analysis aim to examine the effects of autologous epidermal cell suspensions in re-epithelialization of skin lesions. METHODS Relevant articles were retrieved from PubMed, Embase, Cochrane Database, Web of Science, International Clinical Trials Registry Platform, China National Knowledge Infrastructureris, VIP Database for Chinese Technical Periodicals and Wanfang database. The primary output measure was the healing time, and the secondary outputs were effective rate, size of donor site for treatment, size of study treatment area, operation time, pain scores, repigmentation, complications, scar scale scores and satisfaction scores. Data were pooled and expressed as relative risk (RR), mean difference (MD) and standardized mean difference (SMD) with a 95% confidence interval (CI). RESULTS Thirty-one studies were included in this systematic review and meta-analysis, with 914 patients who received autologous epidermal cell suspensions (treatment group) and 883 patients who received standard care or placebo (control group). The pooled data from all included studies demonstrated that the treatment group has significantly reduced healing time (SMD = -0.86; 95% CI: -1.59-0.14; p = 0.02, I2 = 95%), size of donar site for treatment (MD = -115.41; 95% CI: -128.74-102.09; p<0.001, I2 = 89%), operation time (MD = 25.35; 95% CI: 23.42-27.29; p<0.001, I2 = 100%), pain scores (SMD = -1.88; 95% CI: -2.86-0.90; p = 0.0002, I2 = 89%) and complications (RR = 0.59; 95% CI: 0.36-0.96; p = 0.03, I2 = 66%), as well as significantly increased effective rate (RR = 1.20; 95% CI: 1.01-1.42; p = 0.04, I2 = 77%). There were no significant differences in the size of study treatment area, repigmentation, scar scale scores and satisfaction scores between the two groups. CONCLUSION Our meta-analysis showed that autologous epidermal cell suspensions is beneficial for re-epithelialization of skin lesions as they significantly reduce the healing time, size of donar site for treatment, operation time, pain scores and complications, as well as increased effective rate. However, this intervention has minimal impact on size of treatment area, repigmentation, scar scale scores and satisfaction scores.
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Affiliation(s)
- Jiaqi Lou
- Burn DepartmentNingbo No. 2 HospitalNingboZhejiang ProvinceChina
| | - Ziyi Xiang
- Section of Medical PsychologyFaculty of MedicineDepartment of Psychiatry and PsychotherapyUniversity of BonnBonnGermany
| | - Youfen Fan
- Burn DepartmentNingbo No. 2 HospitalNingboZhejiang ProvinceChina
| | - Jingyao Song
- School of Mental HealthWenzhou Medical UniversityWhenzhouZhejiang ProvinceChina
| | - Neng Huang
- Burn DepartmentNingbo No. 2 HospitalNingboZhejiang ProvinceChina
| | - Jiliang Li
- Burn DepartmentNingbo No. 2 HospitalNingboZhejiang ProvinceChina
| | - Guoying Jin
- Burn DepartmentNingbo No. 2 HospitalNingboZhejiang ProvinceChina
| | - Shengyong Cui
- Burn DepartmentNingbo No. 2 HospitalNingboZhejiang ProvinceChina
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Naguib SM, Kassem MA, Hamza HM, Fouda MM, Saleh MK, Hosny KM. Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network. Heliyon 2024; 10:e31017. [PMID: 38803931 PMCID: PMC11128872 DOI: 10.1016/j.heliyon.2024.e31017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Knee Osteoarthritis (OA) is one of the most common joint diseases that may cause physical disability associated with a significant personal and socioeconomic burden. X-ray imaging is the cheapest and most common method to detect Knee (OA). Accurate classification of knee OA can help physicians manage treatment efficiently and slow knee OA progression. This study aims to classify knee OA X-ray images according to anatomical types, such as uni or bicompartmental. The study proposes a deep learning model for classifying uni or bicompartmental knee OA based on redefined residual learning with CNN. The proposed model was trained, validated, and tested on a dataset containing 733 knee X-ray images (331 normal Knee images, 205 unicompartmental, and 197 bicompartmental knee images). The results show 61.81 % and 68.33 % for accuracy and specificity, respectively. Then, the performance of the proposed model was compared with different pre-trained CNNs. The proposed model achieved better results than all pre-trained CNNs.
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Affiliation(s)
- Soaad M. Naguib
- Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
| | - Mohamed A. Kassem
- Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr el-Sheikh, Egypt
| | - Hanaa M. Hamza
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Mohammed K. Saleh
- Department of Orthopedic Surgery, Faculty of Medicine, Zagazig University, Zagazig, 44519, Egypt
| | - Khalid M. Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
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Hermosilla P, Soto R, Vega E, Suazo C, Ponce J. Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review. Diagnostics (Basel) 2024; 14:454. [PMID: 38396492 PMCID: PMC10888121 DOI: 10.3390/diagnostics14040454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/07/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.
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Affiliation(s)
- Pamela Hermosilla
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile (E.V.); (C.S.); (J.P.)
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Venkatesh KP, Raza MM, Nickel G, Wang S, Kvedar JC. Deep learning models across the range of skin disease. NPJ Digit Med 2024; 7:32. [PMID: 38341516 DOI: 10.1038/s41746-024-01033-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Kavitha P, Ayyappan G, Jayagopal P, Mathivanan SK, Mallik S, Al-Rasheed A, Alqahtani MS, Soufiene BO. Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach. BMC Bioinformatics 2023; 24:458. [PMID: 38053030 DOI: 10.1186/s12859-023-05584-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/27/2023] [Indexed: 12/07/2023] Open
Abstract
Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.
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Affiliation(s)
- P Kavitha
- Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, India
| | - G Ayyappan
- Department of Information Technology, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India
| | - Prabhu Jayagopal
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Sandeep Kumar Mathivanan
- School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA
- Department of Pharmacology and Toxicology, The University of Arizona, Tucson, AZ, 85721, USA
| | - Amal Al-Rasheed
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, University of Leicester, Michael Atiyah Building, Leicester, LE1 7RH, UK
| | - Ben Othman Soufiene
- PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
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Akram A, Rashid J, Jaffar MA, Faheem M, Amin RU. Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things. Skin Res Technol 2023; 29:e13524. [PMID: 38009016 PMCID: PMC10646956 DOI: 10.1111/srt.13524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/28/2023] [Indexed: 11/28/2023]
Abstract
INTRODUCTION Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. METHOD This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. RESULTS The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. CONCLUSION In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.
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Affiliation(s)
- Arslan Akram
- Department of Computer Science and Information TechnologySuperior University LahoreLahorePakistan
- MLC Research LabOkaraPakistan
| | - Javed Rashid
- MLC Research LabOkaraPakistan
- Information Technology ServicesUniversity of OkaraOkaraPakistan
| | - Muhammad Arfan Jaffar
- Department of Computer Science and Information TechnologySuperior University LahoreLahorePakistan
| | - Muhammad Faheem
- School of Technology and InnovationsUniversity of VaasaVaasaFinland
| | - Riaz ul Amin
- MLC Research LabOkaraPakistan
- Department of Computer ScienceUniversity of OkaraOkaraPakistan
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Derekas P, Spyridonos P, Likas A, Zampeta A, Gaitanis G, Bassukas I. The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers (Basel) 2023; 15:4861. [PMID: 37835555 PMCID: PMC10571759 DOI: 10.3390/cancers15194861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of the AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (i.e., AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to the low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts of perilesional skin. In total, 16,488 translation-augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-Net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving upon both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.
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Affiliation(s)
- Panagiotis Derekas
- Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece; (P.D.); (A.L.)
| | - Panagiota Spyridonos
- Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Aristidis Likas
- Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece; (P.D.); (A.L.)
| | - Athanasia Zampeta
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
| | - Georgios Gaitanis
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
| | - Ioannis Bassukas
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
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Patel RH, Foltz EA, Witkowski A, Ludzik J. Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review. Cancers (Basel) 2023; 15:4694. [PMID: 37835388 PMCID: PMC10571810 DOI: 10.3390/cancers15194694] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/05/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. OBJECTIVE The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. METHODS A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. RESULTS We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. CONCLUSIONS Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.
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Affiliation(s)
- Raj H. Patel
- Edward Via College of Osteopathic Medicine, VCOM-Louisiana, 4408 Bon Aire Dr, Monroe, LA 71203, USA
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
| | - Emilie A. Foltz
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA
| | - Alexander Witkowski
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
| | - Joanna Ludzik
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
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Hussain M, Khan MA, Damaševičius R, Alasiry A, Marzougui M, Alhaisoni M, Masood A. SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm. Diagnostics (Basel) 2023; 13:2869. [PMID: 37761236 PMCID: PMC10527569 DOI: 10.3390/diagnostics13182869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top-bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.
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Affiliation(s)
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut 13-5053, Lebanon
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan
| | - Robertas Damaševičius
- Center of Excellence Forest 4.0, Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia;
| | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
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Kuo KM, Talley PC, Chang CS. The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis. BMC Med Inform Decis Mak 2023; 23:138. [PMID: 37501114 PMCID: PMC10375663 DOI: 10.1186/s12911-023-02229-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND With rising incidence of skin cancer and relatively increased mortality rates, an improved diagnosis of such a potentially fatal disease is of vital importance. Although frequently curable, it nevertheless places a considerable burden upon healthcare systems. Among the various types of skin cancers, non-melanoma skin cancer is most prevalent. Despite such prevalence and its associated cost, scant proof concerning the diagnostic accuracy via Artificial Intelligence (AI) for non-melanoma skin cancer exists. This study meta-analyzes the diagnostic test accuracy of AI used to diagnose non-melanoma forms of skin cancer, and it identifies potential covariates that account for heterogeneity between extant studies. METHODS Various electronic databases (Scopus, PubMed, ScienceDirect, SpringerLink, and Dimensions) were examined to discern eligible studies beginning from March 2022. Those AI studies predictive of non-melanoma skin cancer were included. Summary estimates of sensitivity, specificity, and area under receiver operating characteristic curves were used to evaluate diagnostic accuracy. The revised Quality Assessment of Diagnostic Studies served to assess any risk of bias. RESULTS A literature search produced 39 eligible articles for meta-analysis. The summary sensitivity, specificity, and area under receiver operating characteristic curve of AI for diagnosing non-melanoma skin cancer was 0.78, 0.98, & 0.97, respectively. Skin cancer typology, data sources, cross validation, ensemble models, types of techniques, pre-trained models, and image augmentation became significant covariates accounting for heterogeneity in terms of both sensitivity and/or specificity. CONCLUSIONS Meta-analysis results revealed that AI is predictive of non-melanoma with an acceptable performance, but sensitivity may become improved. Further, ensemble models and pre-trained models are employable to improve true positive rating.
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Affiliation(s)
- Kuang Ming Kuo
- Department of Business Management, National United University, No.1, Miaoli, 360301, Lienda, Taiwan, Republic of China
| | - Paul C Talley
- Department of Applied English, I-Shou University, No. 1, Sec. 1, Syuecheng Rd., Dashu District, 84001, Kaohsiung City, Taiwan, Republic of China
| | - Chao-Sheng Chang
- Department of Occupational Therapy, I-Shou University, No. 1, Yida Rd., Yanchao District, 82445, Kaohsiung City, Taiwan, Republic of China.
- Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan, Republic of China.
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13
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Khan S, Ali H, Shah Z. Identifying the role of vision transformer for skin cancer-A scoping review. Front Artif Intell 2023; 6:1202990. [PMID: 37529760 PMCID: PMC10388102 DOI: 10.3389/frai.2023.1202990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/03/2023] [Indexed: 08/03/2023] Open
Abstract
Introduction Detecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance. Objective This scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection. Methods The review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included. Results and discussions The review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.
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14
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Peng X, Xu H, Liu J, Wang J, He C. Voice disorder classification using convolutional neural network based on deep transfer learning. Sci Rep 2023; 13:7264. [PMID: 37142759 PMCID: PMC10160111 DOI: 10.1038/s41598-023-34461-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/30/2023] [Indexed: 05/06/2023] Open
Abstract
Voice disorders are very common in the global population. Many researchers have conducted research on the identification and classification of voice disorders based on machine learning. As a data-driven algorithm, machine learning requires a large number of samples for training. However, due to the sensitivity and particularity of medical data, it is difficult to obtain sufficient samples for model learning. To address this challenge, this paper proposes a pretrained OpenL3-SVM transfer learning framework for the automatic recognition of multi-class voice disorders. The framework combines a pre-trained convolutional neural network, OpenL3, and a support vector machine (SVM) classifier. The Mel spectrum of the given voice signal is first extracted and then input into the OpenL3 network to obtain high-level feature embedding. Considering the effects of redundant and negative high-dimensional features, model overfitting easily occurs. Therefore, linear local tangent space alignment (LLTSA) is used for feature dimension reduction. Finally, the obtained dimensionality reduction features are used to train the SVM for voice disorder classification. Fivefold cross-validation is used to verify the classification performance of the OpenL3-SVM. The experimental results show that OpenL3-SVM can effectively classify voice disorders automatically, and its performance exceeds that of the existing methods. With continuous improvements in research, it is expected to be considered as auxiliary diagnostic tool for physicians in the future.
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Affiliation(s)
- Xiangyu Peng
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Huoyao Xu
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Jie Liu
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Junlang Wang
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Chaoming He
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
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15
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Dandu R, Vinayaka Murthy M, Ravi Kumar Y. Transfer learning for segmentation with hybrid classification to Detect Melanoma Skin Cancer. Heliyon 2023; 9:e15416. [PMID: 37151638 PMCID: PMC10161578 DOI: 10.1016/j.heliyon.2023.e15416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 04/06/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023] Open
Abstract
Melanoma is an abnormal proliferation of skin cells that arises and develops in most of the cases on surface of skin that is exposed to copious amounts of sunlight. This common type of cancer may develop in areas of the skin that are not exposed to a much abundant sunlight. The research addresses the problem of Segmentation and Classification of Melanoma Skin Cancer. Melanoma is the fifth most common skin cancer lesion. Bio-medical Imaging and Analysis has become more promising, interesting, and beneficial in recent years to address the eventual problems of Melanoma Skin Cancerous Tissues that may develop on Skin Surfaces. The evolved research finds that Attributes Selected for Classification with Color Layout Filter model. The research has produced an optimal result in terms of certain performance metrics accuracy, precision, recall, PRC (what is PRC? Expansion is needed in Abstract), The proposed method has yielded 90.96% of accuracy and 91% percent of precise and 0.91 of recall out of 1.0, 0.95 of ROC AUC, 0.87 of Kappa Statistic, 0.91 of F-Measure. It has been noticed a lowest error with reference to proposed method on certain dataset. Finally, this research recommends that the Attribute Selected Classifier by implementing one of the image enhancement techniques like Color Layout Filter is showing an efficient outcome.
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16
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Naguib SM, Hamza HM, Hosny KM, Saleh MK, Kassem MA. Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map. Diagnostics (Basel) 2023; 13:diagnostics13071273. [PMID: 37046491 PMCID: PMC10093757 DOI: 10.3390/diagnostics13071273] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/23/2023] [Accepted: 03/25/2023] [Indexed: 03/30/2023] Open
Abstract
Cervical spine (CS) fractures or dislocations are medical emergencies that may lead to more serious consequences, such as significant functional disability, permanent paralysis, or even death. Therefore, diagnosing CS injuries should be conducted urgently without any delay. This paper proposes an accurate computer-aided-diagnosis system based on deep learning (AlexNet and GoogleNet) for classifying CS injuries as fractures or dislocations. The proposed system aims to support physicians in diagnosing CS injuries, especially in emergency services. We trained the model on a dataset containing 2009 X-ray images (530 CS dislocation, 772 CS fractures, and 707 normal images). The results show 99.56%, 99.33%, 99.67%, and 99.33% for accuracy, sensitivity, specificity, and precision, respectively. Finally, the saliency map has been used to measure the spatial support of a specific class inside an image. This work targets both research and clinical purposes. The designed software could be installed on the imaging devices where the CS images are captured. Then, the captured CS image is used as an input image where the designed code makes a clinical decision in emergencies.
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17
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Sun J, Yao K, Huang G, Zhang C, Leach M, Huang K, Yang X. Machine Learning Methods in Skin Disease Recognition: A Systematic Review. Processes (Basel) 2023. [DOI: 10.3390/pr11041003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to collate and analyze machine learning (ML) applications in skin lesion research, with the goal of encouraging the development of automated systems for skin disease diagnosis. To assist dermatologists in their clinical diagnosis, several skin image datasets have been developed and published online. Such efforts have motivated researchers and medical staff to develop automatic skin diagnosis systems using image segmentation and classification processes. This paper summarizes the fundamental steps in skin lesion diagnosis based on papers mainly published since 2013. The applications of ML methods (including traditional ML and deep learning (DL)) in skin disease recognition are reviewed based on their contributions, methods, and achieved results. Such technical analysis is beneficial to the continuing development of reliable and effective computer-aided skin disease diagnosis systems. We believe that more research efforts will lead to the current automatic skin diagnosis studies being used in real clinical settings in the near future.
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18
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An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models. Neural Comput Appl 2023; 35:10695-10716. [PMID: 37155550 PMCID: PMC10015549 DOI: 10.1007/s00521-023-08258-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 01/06/2023] [Indexed: 03/17/2023]
Abstract
Emergency medicine (EM) is one of the attractive research fields in which researchers investigate their efforts to diagnose and treat unforeseen illnesses or injuries. There are many tests and observations are involved in EM. Detection of the level of consciousness is one of these observations, which can be detected using several methods. Among these methods, the automatic estimation of the Glasgow coma scale (GCS) is studied in this paper. The GCS is a medical score used to describe a patient’s level of consciousness. This type of scoring system requires medical examination that may not be available with the shortage of the medical expert. Therefore, the automatic medical calculation for a patient’s level of consciousness is highly needed. Artificial intelligence has been deployed in several applications and appears to have a high performance regarding providing automatic solutions. The main objective of this work is to introduce the edge/cloud system to improve the efficiency of the consciousness measurement through efficient local data processing. Moreover, an efficient machine learning (ML) model to predict the level of consciousness of a certain patient based on the patient’s demographic, vital signs, and laboratory tests is proposed, as well as maintaining the explainability issue using Shapley additive explanations (SHAP) that provides natural language explanation in a form that helps the medical expert to understand the final prediction. The developed ML model is validated using vital signs and laboratory tests extracted from the MIMIC III dataset, and it achieves superior performance (mean absolute error (MAE) = 0.269, mean square error (MSE) = 0.625, R2 score = 0.964). The resulting model is accurate, medically intuitive, and trustworthy.
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19
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A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Comput Biol Med 2023; 157:106726. [PMID: 36924732 DOI: 10.1016/j.compbiomed.2023.106726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/07/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023]
Abstract
Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image classification, detection, and segmentation. Deep learning-based approaches have proven to be quite effective in single lesion recognition and segmentation. Multiple-lesion recognition is more difficult than single-lesion recognition due to the little variation between lesions or the too wide range of lesions involved. Several studies have recently explored deep learning-based algorithms to solve the multiple-lesion recognition challenge. This paper includes an in-depth overview and analysis of deep learning-based methods for multiple-lesion recognition developed in recent years, including multiple-lesion recognition in diverse body areas and recognition of whole-body multiple diseases. We discuss the challenges that still persist in the multiple-lesion recognition tasks by critically assessing these efforts. Finally, we outline existing problems and potential future research areas, with the hope that this review will help researchers in developing future approaches that will drive additional advances.
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20
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Hasan MK, Ahamad MA, Yap CH, Yang G. A survey, review, and future trends of skin lesion segmentation and classification. Comput Biol Med 2023; 155:106624. [PMID: 36774890 DOI: 10.1016/j.compbiomed.2023.106624] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/04/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023]
Abstract
The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.
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Affiliation(s)
- Md Kamrul Hasan
- Department of Bioengineering, Imperial College London, UK; Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
| | - Md Asif Ahamad
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
| | - Choon Hwai Yap
- Department of Bioengineering, Imperial College London, UK.
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, UK; Cardiovascular Research Centre, Royal Brompton Hospital, UK.
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21
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Maqsood S, Damaševičius R. Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare. Neural Netw 2023; 160:238-258. [PMID: 36701878 DOI: 10.1016/j.neunet.2023.01.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/13/2022] [Accepted: 01/19/2023] [Indexed: 01/27/2023]
Abstract
BACKGROUND The idea of smart healthcare has gradually gained attention as a result of the information technology industry's rapid development. Smart healthcare uses next-generation technologies i.e., artificial intelligence (AI) and Internet of Things (IoT), to intelligently transform current medical methods to make them more efficient, dependable and individualized. One of the most prominent uses of telemedicine and e-health in medical image analysis is teledermatology. Telecommunications technologies are used in this industry to send medical information to professionals. Teledermatology is a useful method for the identification of skin lesions, particularly in rural locations, because the skin is visually perceptible. One of the most recent tools for diagnosing skin cancer is dermoscopy. To classify skin malignancies, numerous computational approaches have been proposed in the literature. However, difficulties still exist i.e., lesions with low contrast, imbalanced datasets, high level of memory complexity, and the extraction of redundant features. METHODS In this work, a unified CAD model is proposed based on a deep learning framework for skin lesion segmentation and classification. In the proposed approach, the source dermoscopic images are initially pre-processed using a contrast enhancement based modified bio-inspired multiple exposure fusion approach. In the second stage, a custom 26-layered convolutional neural network (CNN) architecture is designed to segment the skin lesion regions. In the third stage, four pre-trained CNN models (Xception, ResNet-50, ResNet-101 and VGG16) are modified and trained using transfer learning on the segmented lesion images. In the fourth stage, the deep features vectors are extracted from all the CNN models and fused using the convolutional sparse image decomposition fusion approach. In the fifth stage, the univariate measurement and Poisson distribution feature selection approach is used for the best features selection for classification. Finally, the selected features are fed to the multi-class support vector machine (MC-SVM) for the final classification. RESULTS The proposed approach employed to the HAM10000, ISIC2018, ISIC2019, and PH2 datasets and achieved an accuracy of 98.57%, 98.62%, 93.47%, and 98.98% respectively which are better than previous works. CONCLUSION When compared to renowned state-of-the-art methods, experimental results show that the proposed skin lesion detection and classification approach achieved higher performance in terms of both visually and enhanced quantitative evaluation with enhanced accuracy.
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Affiliation(s)
- Sarmad Maqsood
- Department of Software Engineering, Faculty of Informatics Engineering, Kaunas University of Technology, LT-51386 Kaunas, Lithuania.
| | - Robertas Damaševičius
- Department of Software Engineering, Faculty of Informatics Engineering, Kaunas University of Technology, LT-51386 Kaunas, Lithuania.
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22
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Nanofiber-based systems against skin cancers: Therapeutic and protective approaches. J Drug Deliv Sci Technol 2023. [DOI: 10.1016/j.jddst.2023.104367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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23
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Liu Z, Xiong R, Jiang T. CI-Net: Clinical-Inspired Network for Automated Skin Lesion Recognition. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:619-632. [PMID: 36279355 DOI: 10.1109/tmi.2022.3215547] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The lesion recognition of dermoscopy images is significant for automated skin cancer diagnosis. Most of the existing methods ignore the medical perspective, which is crucial since this task requires a large amount of medical knowledge. A few methods are designed according to medical knowledge, but they ignore to be fully in line with doctors' entire learning and diagnosis process, since certain strategies and steps of those are conducted in practice for doctors. Thus, we put forward Clinical-Inspired Network (CI-Net) to involve the learning strategy and diagnosis process of doctors, as for a better analysis. The diagnostic process contains three main steps: the zoom step, the observe step and the compare step. To simulate these, we introduce three corresponding modules: a lesion area attention module, a feature extraction module and a lesion feature attention module. To simulate the distinguish strategy, which is commonly used by doctors, we introduce a distinguish module. We evaluate our proposed CI-Net on six challenging datasets, including ISIC 2016, ISIC 2017, ISIC 2018, ISIC 2019, ISIC 2020 and PH2 datasets, and the results indicate that CI-Net outperforms existing work. The code is publicly available at https://github.com/lzh19961031/Dermoscopy_classification.
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24
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Computer-Aided Diagnosis of Melanoma Subtypes Using Reflectance Confocal Images. Cancers (Basel) 2023; 15:cancers15051428. [PMID: 36900219 PMCID: PMC10000703 DOI: 10.3390/cancers15051428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 03/03/2023] Open
Abstract
Lentigo maligna (LM) is an early form of pre-invasive melanoma that predominantly affects sun-exposed areas such as the face. LM is highly treatable when identified early but has an ill-defined clinical border and a high rate of recurrence. Atypical intraepidermal melanocytic proliferation (AIMP), also known as atypical melanocytic hyperplasia (AMH), is a histological description that indicates melanocytic proliferation with uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may, in some cases, progress to LM. The early diagnosis and distinction of LM from AIMP are important since LM requires a definitive treatment. Reflectance confocal microscopy (RCM) is an imaging technique often used to investigate these lesions non-invasively, without biopsy. However, RCM equipment is often not readily available, nor is the associated expertise for RCM image interpretation easy to find. Here, we implemented a machine learning classifier using popular convolutional neural network (CNN) architectures and demonstrated that it could correctly classify lesions between LM and AIMP on biopsy-confirmed RCM image stacks. We identified local z-projection (LZP) as a recent fast approach for projecting a 3D image into 2D while preserving information and achieved high-accuracy machine classification with minimal computational requirements.
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25
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Caffery LJ, Janda M, Miller R, Abbott LM, Arnold C, Caccetta T, Guitera P, Shumack S, Fernández-Peñas P, Mar V, Soyer HP. Informing a position statement on the use of artificial intelligence in dermatology in Australia. Australas J Dermatol 2023; 64:e11-e20. [PMID: 36380357 DOI: 10.1111/ajd.13946] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 10/06/2022] [Accepted: 10/17/2022] [Indexed: 11/17/2022]
Abstract
Artificial Intelligence (AI) is the ability for computers to simulate human intelligence. In dermatology, there is substantial interest in using AI to identify skin lesions from images. Due to increasing research and interest in the use of AI, the Australasian College of Dermatologists has developed a position statement to inform its members of appropriate use of AI. This article presents the ACD Position Statement on the use of AI in dermatology, and provides explanatory information that was used to inform the development of this statement.
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Affiliation(s)
- Liam J Caffery
- Centre for Online Health, The University of Queensland, Brisbane, Queensland, Australia.,Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia.,The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
| | - Monika Janda
- Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia
| | - Robert Miller
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia
| | - Lisa M Abbott
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Chris Arnold
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,Australasian Society of Cosmetic Dermatologists, South Yarra, Victoria, Australia.,BioGrid Australia, Parkville, Victoria, Australia
| | - Tony Caccetta
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia
| | - Pascale Guitera
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia
| | - Stephen Shumack
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,The University of Sydney, Sydney, New South Wales, Australia
| | - Pablo Fernández-Peñas
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,The University of Sydney, Sydney, New South Wales, Australia
| | - Victoria Mar
- The Australasian College of Dermatologists, Sydney, New South Wales, Australia.,Victorian Melanoma Service, Alfred Health, Victoria, Australia.,School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - H Peter Soyer
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
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26
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Malignant melanoma diagnosis applying a machine learning method based on the combination of nonlinear and texture features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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27
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Baig AR, Abbas Q, Almakki R, Ibrahim MEA, AlSuwaidan L, Ahmed AES. Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions. Diagnostics (Basel) 2023; 13:diagnostics13030385. [PMID: 36766490 PMCID: PMC9914027 DOI: 10.3390/diagnostics13030385] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
Skin cancer develops due to the unusual growth of skin cells. Early detection is critical for the recognition of multiclass pigmented skin lesions (PSLs). At an early stage, the manual work by ophthalmologists takes time to recognize the PSLs. Therefore, several "computer-aided diagnosis (CAD)" systems are developed by using image processing, machine learning (ML), and deep learning (DL) techniques. Deep-CNN models outperformed traditional ML approaches in extracting complex features from PSLs. In this study, a special transfer learning (TL)-based CNN model is suggested for the diagnosis of seven classes of PSLs. A novel approach (Light-Dermo) is developed that is based on a lightweight CNN model and applies the channelwise attention (CA) mechanism with a focus on computational efficiency. The ShuffleNet architecture is chosen as the backbone, and squeeze-and-excitation (SE) blocks are incorporated as the technique to enhance the original ShuffleNet architecture. Initially, an accessible dataset with 14,000 images of PSLs from seven classes is used to validate the Light-Dermo model. To increase the size of the dataset and control its imbalance, we have applied data augmentation techniques to seven classes of PSLs. By applying this technique, we collected 28,000 images from the HAM10000, ISIS-2019, and ISIC-2020 datasets. The outcomes of the experiments show that the suggested approach outperforms compared techniques in many cases. The most accurately trained model has an accuracy of 99.14%, a specificity of 98.20%, a sensitivity of 97.45%, and an F1-score of 98.1%, with fewer parameters compared to state-of-the-art DL models. The experimental results show that Light-Dermo assists the dermatologist in the better diagnosis of PSLs. The Light-Dermo code is available to the public on GitHub so that researchers can use it and improve it.
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28
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Shen J, Hu Y, Zhang X, Gong Y, Kawasaki R, Liu J. Structure-Oriented Transformer for retinal diseases grading from OCT images. Comput Biol Med 2023; 152:106445. [PMID: 36549031 DOI: 10.1016/j.compbiomed.2022.106445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 11/23/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Retinal diseases are the leading causes of vision temporary or permanent loss. Precise retinal disease grading is a prerequisite for early intervention or specific therapeutic schedules. Existing works based on Convolutional Neural Networks (CNN) focus on typical locality structures and cannot capture long-range dependencies. But retinal disease grading relies more on the relationship between the local lesion and the whole retina, which is consistent with the self-attention mechanism. Therefore, the paper proposes a novel Structure-Oriented Transformer (SoT) framework to further construct the relationship between lesions and retina on clinical datasets. To reduce the dependence on the amount of data, we design structure guidance as a model-oriented filter to emphasize the whole retina structure and guide relation construction. Then, we adopt the pre-trained vision transformer that efficiently models all feature patches' relationships via transfer learning. Besides, to make the best of all output tokens, a Token vote classifier is proposed to obtain the final grading results. We conduct extensive experiments on one clinical neovascular Age-related Macular Degeneration (nAMD) dataset. The experiments demonstrate the effectiveness of SoT components and improve the ability of relation construction between lesion and retina, which outperforms the state-of-the-art methods for nAMD grading. Furthermore, we evaluate our SoT on one publicly available retinal diseases dataset, which proves our algorithm has classification superiority and good generality.
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Affiliation(s)
- Junyong Shen
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 51805, Guangdong, China
| | - Yan Hu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 51805, Guangdong, China.
| | - Xiaoqing Zhang
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 51805, Guangdong, China
| | - Yan Gong
- Ningbo Eye hospital, Ningbo, 315000, Zhenjiang, China
| | - Ryo Kawasaki
- Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 51805, Guangdong, China; Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 51805, Guangdong, China.
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Spyridonos P, Gaitanis G, Likas A, Bassukas ID. A convolutional neural network based system for detection of actinic keratosis in clinical images of cutaneous field cancerization. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ding Y, Yi Z, Li M, long J, Lei S, Guo Y, Fan P, Zuo C, Wang Y. HI-MViT: A lightweight model for explainable skin disease classification based on modified MobileViT. Digit Health 2023; 9:20552076231207197. [PMID: 37846401 PMCID: PMC10576942 DOI: 10.1177/20552076231207197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/26/2023] [Indexed: 10/18/2023] Open
Abstract
Objective To develop an explainable lightweight skin disease high-precision classification model that can be deployed to the mobile terminal. Methods In this study, we present HI-MViT, a lightweight network for explainable skin disease classification based on Modified MobileViT. HI-MViT is mainly composed of ordinary convolution, Improved-MV2, MobileViT block, global pooling, and fully connected layers. Improved-MV2 uses the combination of shortcut and depth classifiable convolution to substantially decrease the amount of computation while ensuring the efficient implementation of information interaction and memory. The MobileViT block can efficiently encode local and global information. In addition, semantic feature dimensionality reduction visualization and class activation mapping visualization methods are used for HI-MViT to further understand the attention area of the model when learning skin lesion images. Results The International Skin Imaging Collaboration has assembled and made available the ISIC series dataset. Experiments using the HI-MViT model on the ISIC-2018 dataset achieved scores of 0.931, 0.932, 0.961, and 0.977 on F1-Score, Accuracy, Average Precision (AP), and area under the curve (AUC). Compared with the top five algorithms of ISIC-2018 Task 3, Marco's average F1-Score, AP, and AUC have increased by 6.9%, 6.8%, and 0.8% compared with the suboptimal performance model. Compared with ConvNeXt, the most competitive convolutional neural network architecture, our model is 5.0%, 3.4%, 2.3%, and 2.2% higher in F1-Score, Accuracy, AP, and AUC, respectively. The experiments on the ISIC-2017 dataset also achieved excellent results, and all indicators were better than the top five algorithms of ISIC-2017 Task 3. Using the trained model to test on the PH2 dataset, an excellent performance score is obtained, which shows that it has good generalization performance. Conclusions The skin disease classification model HI-MViT proposed in this article shows excellent classification performance and generalization performance in experiments. It demonstrates how the classification outcomes can be applied to dermatologists' computer-assisted diagnostics, enabling medical professionals to classify various dermoscopic images more rapidly and reliably.
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Affiliation(s)
- Yuhan Ding
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Zhenglin Yi
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Departments of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Mengjuan Li
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jianhong long
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Shaorong Lei
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yu Guo
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Pengju Fan
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Chenchen Zuo
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yongjie Wang
- Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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31
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Nancy Jane Y, Charanya SK, Amsaprabhaa M, Jayashanker P, Nehemiah H K. 2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma. Comput Biol Med 2023; 152:106333. [PMID: 36463793 DOI: 10.1016/j.compbiomed.2022.106333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/20/2022]
Abstract
Melanoma is a fatal form of skin cancer, which causes excess skin cell growth in the body. The objective of this work is to develop a two-tier hybrid dual convolution neural network (2-HDCNN) feature fusion approach for malignant melanoma prediction. The first-tier baseline Convolutional Neural Network (CNN) extracts the hard to classify samples based on the confidence factor (class probability variance score) and generates a Baseline Segregated Dataset (BSD). The BSD is then preprocessed using hair removal and data augmentation techniques. The preprocessed BSD is trained with the second-tier CNN that yields the bottleneck features. These features are then combined with the derived features from the ABCD (Asymmetry, Border, Color and Diameter) medical rule to improve classification accuracy. The generated hybrid fused features are fed to different classifiers like Gradient boosting classifiers, Bagging classifiers, XGBoost classifiers, Decision trees, Support Vector Machine, Logistic regression and Multi-layer perceptron. For performance assessment, the proposed framework is trained on the ISIC 2018 dataset. The experimental results prove that the presented 2-HDCNN feature fusion approach has reached an accuracy of 92.15%, precision of 96.96%, specificity of 96.8%, sensitivity of 86.48%, and AUC (Area Under Curve) value of 0.96 for diagnosing malignant melanoma.
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Affiliation(s)
- Y Nancy Jane
- Department of Computer Technology, Madras Institute of Technology (Anna University), Chennai, 600044, India
| | - S K Charanya
- Department of Computer Technology, Madras Institute of Technology (Anna University), Chennai, 600044, India
| | - M Amsaprabhaa
- Department of Computer Technology, Madras Institute of Technology (Anna University), Chennai, 600044, India
| | - Preetiha Jayashanker
- Department of Computer Technology, Madras Institute of Technology (Anna University), Chennai, 600044, India
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A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions with Swish Activations. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:1847115. [PMID: 36794097 PMCID: PMC9925248 DOI: 10.1155/2023/1847115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/16/2022] [Accepted: 11/24/2022] [Indexed: 02/08/2023]
Abstract
Skin cancer remains one of the deadliest kinds of cancer, with a survival rate of about 18-20%. Early diagnosis and segmentation of the most lethal kind of cancer, melanoma, is a challenging and critical task. To diagnose medicinal conditions of melanoma lesions, different researchers proposed automatic and traditional approaches to accurately segment the lesions. However, visual similarity among lesions and intraclass differences are very high, which leads to low-performance accuracy. Furthermore, traditional segmentation algorithms often require human inputs and cannot be utilized in automated systems. To address all of these issues, we provide an improved segmentation model based on depthwise separable convolutions that act on each spatial dimension of the image to segment the lesions. The fundamental idea behind these convolutions is to divide the feature learning steps into two simpler parts that are spatial learning of features and a step for channel combination. Besides this, we employ parallel multidilated filters to encode multiple parallel features and broaden the view of filters with dilations. Moreover, for performance evaluation, the proposed approach is evaluated on three different datasets including DermIS, DermQuest, and ISIC2016. The finding indicates that the suggested segmentation model has achieved the Dice score of 97% for DermIS and DermQuest and 94.7% for the ISBI2016 dataset, respectively.
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33
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Yin W, Huang J, Chen J, Ji Y. A study on skin tumor classification based on dense convolutional networks with fused metadata. Front Oncol 2022; 12:989894. [PMID: 36601473 PMCID: PMC9806866 DOI: 10.3389/fonc.2022.989894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
Skin cancer is the most common cause of death in humans. Statistics show that competent dermatologists have a diagnostic accuracy rate of less than 80%, while inexperienced dermatologists have a diagnostic accuracy rate of less than 60%. The higher rate of misdiagnosis will cause many patients to miss the most effective treatment window, risking the patients' life safety. However, the majority of the current study of neural network-based skin cancer diagnosis remains at the image level without patient clinical data. A deep convolutional network incorporating clinical patient metadata of skin cancer is presented to realize the classification model of skin cancer in order to further increase the accuracy of skin cancer diagnosis. There are three basic steps in the approach. First, the high-level features (edge features, color features, texture features, form features, etc.). Implied by the image were retrieved using the pre-trained DenseNet-169 model on the ImageNet dataset. Second, the MetaNet module is introduced, which uses metadata to control a certain portion of each feature channel in the DenseNet-169 network in order to produce weighted features. The MetaBlock module was added at the same time to improve the features retrieved from photos using metadata, choosing the most pertinent characteristics in accordance with the metadata data. The features of the MetaNet and MetaBlock modules were finally combined to create the MD-Net module, which was then used as input into the classifier to get the classification results for skin cancers. On the PAD-UFES-20 and ISIC 2019 datasets, the suggested methodology was assessed. The DenseNet-169 network model combined with this module, according to experimental data, obtains 81.4% in the balancing accuracy index, and its diagnostic accuracy is up between 8% and 15.6% compared to earlier efforts. Additionally, it solves the problem of actinic keratosis and poorly classified skin fibromas.
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Affiliation(s)
- Wenjun Yin
- School of Information and Communication, Guilin University Of Electronic Technology, Guilin, China
| | - Jianhua Huang
- School of Information and Communication, Guilin University Of Electronic Technology, Guilin, China,*Correspondence: Jianhua Huang, ; Jianlin Chen,
| | - Jianlin Chen
- Reproductive Endocrinology Clinic, Second Xiangya Hospital of Central South University, Changsha, China,*Correspondence: Jianhua Huang, ; Jianlin Chen,
| | - Yuanfa Ji
- School of Information and Communication, Guilin University Of Electronic Technology, Guilin, China
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Vera J, Lai X, Baur A, Erdmann M, Gupta S, Guttà C, Heinzerling L, Heppt MV, Kazmierczak PM, Kunz M, Lischer C, Pützer BM, Rehm M, Ostalecki C, Retzlaff J, Witt S, Wolkenhauer O, Berking C. Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence. Brief Bioinform 2022; 23:6761961. [PMID: 36252807 DOI: 10.1093/bib/bbac433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/28/2022] [Accepted: 09/08/2022] [Indexed: 12/19/2022] Open
Abstract
We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.
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Affiliation(s)
- Julio Vera
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Xin Lai
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Andreas Baur
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Michael Erdmann
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Cristiano Guttà
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Lucie Heinzerling
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany.,Department of Dermatology, LMU University Hospital, Munich, Germany
| | - Markus V Heppt
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany
| | - Christopher Lischer
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, 18057 Rostock, Germany
| | - Markus Rehm
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany.,Stuttgart Research Center Systems Biology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Christian Ostalecki
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Jimmy Retzlaff
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Carola Berking
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
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Yang X, Ye X, Zhao D, Heidari AA, Xu Z, Chen H, Li Y. Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization. Front Neuroinform 2022; 16:1041799. [PMID: 36387585 PMCID: PMC9663822 DOI: 10.3389/fninf.2022.1041799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 10/10/2022] [Indexed: 11/07/2022] Open
Abstract
Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain much information. However, the percentage of the critical information in the image is small, and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional histogram approach to the above problem. We present an enhanced ant colony optimization for continuous domains (EACOR) in the proposed model based on the soft besiege and chase strategies. Further, EACOR is combined with two-dimensional Kapur’s entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also conducted several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.
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Affiliation(s)
- Xiao Yang
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China
| | - Xiaojia Ye
- School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China
- *Correspondence: Xiaojia Ye,
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
- Dong Zhao,
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Zhangze Xu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Yangyang Li
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Yangyang Li,
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36
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Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9086060. [PMID: 36262625 PMCID: PMC9576372 DOI: 10.1155/2022/9086060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/21/2022] [Accepted: 08/29/2022] [Indexed: 11/20/2022]
Abstract
Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is one of the most diagnosed cancers in women worldwide. Breast cancer may be detected and diagnosed using imaging methods such as histopathological images. Since various tissues make up the breast, there is a wide range of textural intensity, making abnormality detection difficult. As a result, there is an urgent need to improve computer-assisted systems (CAD) that can serve as a second opinion for radiologists when they use medical images. A self-training learning method employing deep learning neural network with residual learning is proposed to overcome the issue of needing a large number of labeled images to train deep learning models in breast cancer histopathology image classification. The suggested model is built from scratch and trained.
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Ding H, Zhang E, Fang F, Liu X, Zheng H, Yang H, Ge Y, Yang Y, Lin T. Automatic identification of benign pigmented skin lesions from clinical images using deep convolutional neural network. BMC Biotechnol 2022; 22:28. [PMID: 36217185 PMCID: PMC9552359 DOI: 10.1186/s12896-022-00755-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 08/26/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE We aimed to develop a computer-aided detection (CAD) system for accurate identification of benign pigmented skin lesions (PSLs) from images captured using a digital camera or a smart phone. METHODS We collected a total of 12,836 clinical images which had been classified and location-labeled for training and validating. Four models were developed and validated; you only look once, v4 (YOLOv4), you only look once, v5 (YOLOv5), single shot multibox detector (SSD) and faster region-based convolutional neural networks (Faster R-CNN). The performance of the models was compared with three trained dermatologists, respectively. The accuracy of the best model was further tested and validated using smartphone-captured images. RESULTS The accuracies of YOLOv4, YOLOv5, SSD and Faster R-CNN were 0.891, 0.929, 0.852 and 0.874, respectively. The precision, sensitivity and specificity of YOLOv5 (the best model) were 0.956, 0.962 and 0.952, respectively. The accuracy of YOLOv5 model for images captured using a smart-phone was 0.905. The CAD based YOLOv5 system can potentially be used in clinical identification of PSLs. CONCLUSION We developed and validated a CAD system for automatic identification of benign PSLs using digital images. This approach may be used by non-dermatologists to easily diagnose by taking a photo of skin lesion and guide on management of PSLs.
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Affiliation(s)
- Hui Ding
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China
| | - Eejia Zhang
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China
| | - Fumin Fang
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China
| | - Xing Liu
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China
| | - Huiying Zheng
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China
| | - Hedan Yang
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China
| | - Yiping Ge
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China.
| | - Yin Yang
- Department of Cosmetic Laser Surgery, Hospital for Skin Disease and Institute of Dermatology, Peking Union Medical College and Chinese Academy of Medical Sciences (CAMS), Nanjing, 210042, China.
| | - Tong Lin
- Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
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Wako BD, Dese K, Ulfata RE, Nigatu TA, Turunbedu SK, Kwa T. Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning. Cancer Control 2022; 29:10732748221132528. [PMID: 36194624 PMCID: PMC9536105 DOI: 10.1177/10732748221132528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Objectives Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts’ experience which may lead to misdiagnosis and mistreatment plans. This study aims to develop a system for the automatic diagnosis of skin cancer margin for squamous cell carcinoma from histopathology microscopic images by applying deep learning techniques. Methods The system was trained, validated, and tested using histopathology images of SCC cancer locally acquired from Jimma Medical Center Pathology Department from seven different skin sites using an Olympus digital microscope. All images were preprocessed and trained with transfer learning pre-trained models by fine-tuning the hyper-parameter of the selected models. Results The overall best training accuracy of the models become 95.3%, 97.1%, 89.8%, and 89.9% on EffecientNetB0, MobileNetv2, ResNet50, VGG16 respectively. In addition to this, the best validation accuracy of the models was 94.7%, 91.8%, 87.8%, and 86.7% respectively. The best testing accuracy of the models at the same epoch was 95.2%, 91.5%, 87%, and 85.5% respectively. From these models, EfficientNetB0 showed the best average training and testing accuracy than the other models. Conclusions The system assists the pathologist during the margin assessment of SCC by decreasing the diagnosis time from an average of 25 minutes to less than a minute.
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Affiliation(s)
- Beshatu Debela Wako
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Center of Biomedical Engineering, Jimma University Medical Center, Jimma, Ethiopia
| | - Kokeb Dese
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Artificial Intelligence and Biomedical Imaging Research Lab, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Kokeb Dese, Department of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia. ,
| | - Roba Elala Ulfata
- Department of Pathology, Jimma Institute of Health, Jimma University, Jimma, Ethiopia,Department of Pathology, Adama General Hospital and Medical College, Adama, Ethiopia
| | - Tilahun Alemayehu Nigatu
- Department of Biomedical Sciences (Anatomy Course Unit), Jimma Institute of Health, Jimma University, Jimma, Ethiopia
| | | | - Timothy Kwa
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Department of Biomedical Engineering, University of California, 451 Health Sciences, Davis, CA, USA,Medtronic MiniMed, 18000 Devonshire St., Northridge, Los Angeles, CA, USA,Timothy Kwa, Department of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia.
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Short WD, Olutoye OO, Padon BW, Parikh UM, Colchado D, Vangapandu H, Shams S, Chi T, Jung JP, Balaji S. Advances in non-invasive biosensing measures to monitor wound healing progression. Front Bioeng Biotechnol 2022; 10:952198. [PMID: 36213059 PMCID: PMC9539744 DOI: 10.3389/fbioe.2022.952198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/12/2022] [Indexed: 01/09/2023] Open
Abstract
Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed.
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Affiliation(s)
- Walker D. Short
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Oluyinka O. Olutoye
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Benjamin W. Padon
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Umang M. Parikh
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Daniel Colchado
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Hima Vangapandu
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Shayan Shams
- Department of Applied Data Science, San Jose State University, San Jose, CA, United States
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
| | - Taiyun Chi
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Jangwook P. Jung
- Department of Biological Engineering, Louisiana State University, Baton Rouge, LA, United States
| | - Swathi Balaji
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
- *Correspondence: Swathi Balaji,
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40
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Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern. PLoS One 2022; 17:e0274896. [PMID: 36126072 PMCID: PMC9488768 DOI: 10.1371/journal.pone.0274896] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/06/2022] [Indexed: 11/26/2022] Open
Abstract
Skin cancer is the most common type of cancer in many parts of the world. As skin cancers start as skin lesions, it is important to identify precancerous skin lesions early. In this paper we propose an image based skin lesion identification to classify seven different classes of skin lesions. First, Multi Resolution Empirical Mode Decomposition (MREMD) is used to decompose each skin lesion image into a few Bidimensional intrinsic mode functions (BIMF). MREMD is a simplified bidimensional empirical mode decomposition (BEMD) that employs downsampling and upsampling (interpolation) in the upper and lower envelope formation to speed up the decomposition process. A few BIMFs are extracted from the image using MREMD. The next step is to locate the lesion or the region of interest (ROI) in the image using active contour. Then Local Binary Pattern (LBP) is applied to the ROI of the image and its first BIMF to extract a total of 512 texture features from the lesion area. In the training phase, texture features of seven different classes of skin lesions are used to train an Artificial Neural Network (ANN) classifier. Altogether, 490 images from HAM10000 dataset are used to train the ANN. Then the accuracy of the approach is evaluated using 315 test images that are different from the training images. The test images are taken from the same dataset and each test image contains one type of lesion from the seven types that are classified. From each test image, 512 texture features are extracted from the lesion area and introduced to the classifier to determine its class. The proposed method achieves an overall classification rate of 98.9%.
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Yilmaz A, Gencoglan G, Varol R, Demircali AA, Keshavarz M, Uvet H. MobileSkin: Classification of Skin Lesion Images Acquired Using Mobile Phone-Attached Hand-Held Dermoscopes. J Clin Med 2022; 11:jcm11175102. [PMID: 36079042 PMCID: PMC9457478 DOI: 10.3390/jcm11175102] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/17/2022] [Accepted: 08/26/2022] [Indexed: 11/16/2022] Open
Abstract
Dermoscopy is the visual examination of the skin under a polarized or non-polarized light source. By using dermoscopic equipment, many lesion patterns that are invisible under visible light can be clearly distinguished. Thus, more accurate decisions can be made regarding the treatment of skin lesions. The use of images collected from a dermoscope has both increased the performance of human examiners and allowed the development of deep learning models. The availability of large-scale dermoscopic datasets has allowed the development of deep learning models that can classify skin lesions with high accuracy. However, most dermoscopic datasets contain images that were collected from digital dermoscopic devices, as these devices are frequently used for clinical examination. However, dermatologists also often use non-digital hand-held (optomechanical) dermoscopes. This study presents a dataset consisting of dermoscopic images taken using a mobile phone-attached hand-held dermoscope. Four deep learning models based on the MobileNetV1, MobileNetV2, NASNetMobile, and Xception architectures have been developed to classify eight different lesion types using this dataset. The number of images in the dataset was increased with different data augmentation methods. The models were initialized with weights that were pre-trained on the ImageNet dataset, and then they were further fine-tuned using the presented dataset. The most successful models on the unseen test data, MobileNetV2 and Xception, had performances of 89.18% and 89.64%. The results were evaluated with the 5-fold cross-validation method and compared. Our method allows for automated examination of dermoscopic images taken with mobile phone-attached hand-held dermoscopes.
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Affiliation(s)
- Abdurrahim Yilmaz
- Mechatronics Engineering, Yildiz Technical University, 34349 Istanbul, Turkey
- Department of Business Administration, Bundeswehr University Munich, 85579 Munich, Germany
| | - Gulsum Gencoglan
- Department of Dermatology, Liv Hospital Vadistanbul, Istinye University, 34396 Istanbul, Turkey
| | - Rahmetullah Varol
- Mechatronics Engineering, Yildiz Technical University, 34349 Istanbul, Turkey
- Department of Business Administration, Bundeswehr University Munich, 85579 Munich, Germany
| | - Ali Anil Demircali
- Department of Metabolism, Digestion and Reproduction, The Hamlyn Centre, Imperial College London, Bessemer Building, London SW7 2AZ, UK
| | - Meysam Keshavarz
- Department of Electrical and Electronic Engineering, The Hamlyn Centre, Imperial College London, Bessemer Building, London SW7 2AZ, UK
- Correspondence: (M.K.); (H.U.)
| | - Huseyin Uvet
- Mechatronics Engineering, Yildiz Technical University, 34349 Istanbul, Turkey
- Correspondence: (M.K.); (H.U.)
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42
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Special Issue on “Advances in Skin Lesion Image Analysis Using Machine Learning Approaches”. Diagnostics (Basel) 2022; 12:diagnostics12081928. [PMID: 36010278 PMCID: PMC9406302 DOI: 10.3390/diagnostics12081928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/06/2022] [Indexed: 11/23/2022] Open
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43
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Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there is a need for sample-efficient learning techniques, particularly in the field of medical image analysis, as significant cost and effort are required to obtain a sufficient number of well-annotated high-quality training samples. In this paper, we address the problem of deep neural network training under sample deficiency by investigating several sample-efficient deep learning techniques. We concentrate on applying these techniques to skin burn image analysis and classification. We first build a large-scale, professionally annotated dataset of skin burn images, which enables the establishment of convolutional neural network (CNN) models for burn severity assessment with high accuracy. We then deliberately set data limitation conditions and adapt several sample-efficient techniques, such as transferable learning (TL), self-supervised learning (SSL), federated learning (FL), and generative adversarial network (GAN)-based data augmentation, to those conditions. Through comprehensive experimentation, we evaluate the sample-efficient deep learning techniques for burn severity assessment, and show, in particular, that SSL models learned on a small task-specific dataset can achieve comparable accuracy to a baseline model learned on a six-times larger dataset. We also demonstrate the applicability of FL and GANs to model training under different data limitation conditions that commonly occur in the area of healthcare and medicine where deep learning models are adopted.
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A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:6805460. [PMID: 35845738 PMCID: PMC9259277 DOI: 10.1155/2022/6805460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/01/2022] [Accepted: 06/02/2022] [Indexed: 12/11/2022]
Abstract
The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model's classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.
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45
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Gouda W, Sama NU, Al-Waakid G, Humayun M, Jhanjhi NZ. Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning. Healthcare (Basel) 2022; 10:healthcare10071183. [PMID: 35885710 PMCID: PMC9324455 DOI: 10.3390/healthcare10071183] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 12/12/2022] Open
Abstract
An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study’s innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.
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Affiliation(s)
- Walaa Gouda
- Department of Computer Engineering and Network, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
- Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 4272077, Egypt
- Correspondence: (W.G.); (M.H.)
| | - Najm Us Sama
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia;
| | - Ghada Al-Waakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia;
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
- Correspondence: (W.G.); (M.H.)
| | - Noor Zaman Jhanjhi
- School of Computer Science and Engineering (SCE), Taylor’s University, Subang Jaya 47500, Malaysia;
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46
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Buonomo M, Fiessinger L, Goldfarb N, Jaimes JP. Orthostatic changes in dermatoscopy. J Eur Acad Dermatol Venereol 2022; 36:e908-e910. [PMID: 35734949 DOI: 10.1111/jdv.18355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Michele Buonomo
- University of Minnesota Medical School, Minneapolis, MN, USA
| | - Lori Fiessinger
- University of Minnesota, Department of Dermatology, Minneapolis, MN, USA
| | - Noah Goldfarb
- University of Minnesota, Departments of Internal Medicine and Dermatology, Minneapolis, MN, USA.,Minneapolis VA Medical Center, Departments of Medicine and Dermatology, Minneapolis, MN, USA
| | - Juan P Jaimes
- University of Minnesota, Department of Dermatology, Minneapolis, MN, USA
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47
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Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food. J FOOD QUALITY 2022. [DOI: 10.1155/2022/5890666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Skin cancer is one of the most common types of cancer that has a high mortality rate. Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer. Each year, approximately 55% of individuals die due to skin cancer. Early detection of skin cancer enhances the survival rate of individuals. There are various antioxidants like vitamins C, E, and A, zinc, and selenium present in various foods that can be helpful in preventing skin cancer. “Deep Learning” (DL) is an effective method to detect cancerous lesions. The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. The present research analyzes the accuracy, sensitivity, and specificity of “Convolutional Neural Network” (CNN) for DL in the early detection of skin cancer. A statistical analysis has been done with IBM SPSS software to understand how the accuracy, sensitivity, and specificity of CNN change with the change in image number, augmentation number, epochs, and resolution of images. These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables. After that, a linear regression analysis was carried out to obtain t and
values. The major scope of the study is to analyze the major role played by the DL models through the big data network in the medical industry. The researchers also found that when additional characteristics are present, image resolution does not have the potential to reduce image accuracy, specificity, or sensitivity. The scope of the study is more focused on using a DL-based big data network for supporting healthcare workers in detecting skin cancer at an early stage and the role of technology in supporting medical practitioners in rendering better treatment. Findings showed that the number of training images increases the accuracy, sensitivity, and specificity of CNN architecture when various and effective augmentation techniques are used. Image resolution did not show any significant relationship with accuracy. The number of epochs positively affected the accuracy, sensitivity, and specificity; however, more than 98% accuracy has been observed with epochs between 50 and 70.
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48
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Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4928096. [PMID: 35694573 PMCID: PMC9184172 DOI: 10.1155/2022/4928096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022]
Abstract
Multiple sclerosis (MS) is an autoimmune disease that causes mild to severe issues in the central nervous system (CNS). Early detection and treatment are necessary to reduce the harshness of the disease in individuals. The proposed work aims to implement a convolutional neural network (CNN) segmentation scheme to extract the MS lesion in a 2D brain MRI slice. To achieve a better MS detection, this work implemented the VGG-UNet scheme in which the pretrained VGG19 is considered as the encoder section. This scheme is tested on 30 patient images (600 images with dimension 512 × 512 × 3 pixels), and the experimental outcome confirms that this scheme provides a better result compared to traditional UNet, SegNet, VGG-UNet, and VGG-SegNet. The experimental investigation implemented on axial, coronal and sagittal plane 2D slices of Flair modality confirms that this work provides a better value of Jaccard (>85%), Dice (>92%), and accuracy (>98%).
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49
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Fitzsimmons L, Dewan M, Dexheimer JW. Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications. Appl Clin Inform 2022; 13:569-582. [PMID: 35613914 DOI: 10.1055/s-0042-1749119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
OBJECTIVE As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. METHODS We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. RESULTS From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. DISCUSSION With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. CONCLUSION As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.
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Affiliation(s)
- Lane Fitzsimmons
- College of Agriculture and Life Science, Cornell University, Ithaca, New York, United States
| | - Maya Dewan
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
| | - Judith W Dexheimer
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States.,Division of Emergency Medicine; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
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50
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Liu X, Krylov V, Jun S, Volkova N, Sachenko A, Shcherbakova G, Woloszyn J. Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:6923-6939. [PMID: 35730289 DOI: 10.3934/mbe.2022326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved vector-difference method for spectral-statistical texture segmentation has been developed. The method is based on the estimation of the number of features and subsequent calculation of a specific texture feature, and it uses wavelets obtained by transforming the graph of the power function at the stage of contour segmentation. Based on the above, the authors developed a modulus for spectral-statistical texture segmentation, which they applied to segment images of psoriatic disease; the Pratt's criterion was used to assess the quality of segmentation. The reliability of the classification of the spectral-statistical texture images was confirmed by using the True Positive Rate (TPR) and False Positive Rate (FPR) metrics calculated on the basis of the confusion matrix. The results of the experimental research confirmed the advantage of the proposed vector-difference method for the segmentation of spectral-statistical textures. The method enables further supplementation of the vector of features at the stage of identification through the use of the most informative features based on characteristic points for different degrees and types of psoriatic disease.
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Affiliation(s)
- Xinlin Liu
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Viktor Krylov
- Department of Applied Mathematics and Information Technologies, Odessa National Polytechnic University, Odessa 65044, Ukraine
| | - Su Jun
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Natalya Volkova
- Department of Applied Mathematics and Information Technologies, Odessa National Polytechnic University, Odessa 65044, Ukraine
| | - Anatoliy Sachenko
- Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland
- Research Institute for Intelligent Computer Systems, West Ukrainian National University, Ternopil 46009, Ukraine
| | - Galina Shcherbakova
- Department of Information Systems, Odessa National Polytechnic University, Odessa 65044, Ukraine
| | - Jacek Woloszyn
- Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland
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