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Slominski RM, Kim TK, Janjetovic Z, Brożyna AA, Podgorska E, Dixon KM, Mason RS, Tuckey RC, Sharma R, Crossman DK, Elmets C, Raman C, Jetten AM, Indra AK, Slominski AT. Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling. Cancers (Basel) 2024; 16:2262. [PMID: 38927967 PMCID: PMC11201527 DOI: 10.3390/cancers16122262] [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: 05/04/2024] [Revised: 06/09/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem.
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Affiliation(s)
- Radomir M. Slominski
- Department of Rheumatology and Clinical Immunology, Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Tae-Kang Kim
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Zorica Janjetovic
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Anna A. Brożyna
- Department of Human Biology, Institute of Biology, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, 87-100 Torun, Poland;
| | - Ewa Podgorska
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Katie M. Dixon
- School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia; (K.M.D.); (R.S.M.)
| | - Rebecca S. Mason
- School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia; (K.M.D.); (R.S.M.)
| | - Robert C. Tuckey
- School of Molecular Sciences, University of Western Australia, Perth, WA 6009, Australia;
| | - Rahul Sharma
- Department of Biomedical Informatics and Data Science, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - David K. Crossman
- Department of Genetics and Bioinformatics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Craig Elmets
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Chander Raman
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
| | - Anton M. Jetten
- Cell Biology Section, NIEHS—National Institutes of Health, Research Triangle Park, NC 27709, USA;
| | - Arup K. Indra
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR 97331, USA
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Andrzej T. Slominski
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.-K.K.); (Z.J.); (E.P.); (C.E.); (C.R.)
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Pathology and Laboratory Medicine Service, Veteran Administration Medical Center, Birmingham, AL 35233, USA
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Chen M, Zhou AE, Jain N, Gronbeck C, Feng H, Grant-Kels JM. Ethics of artificial intelligence in dermatology. Clin Dermatol 2024; 42:313-316. [PMID: 38401700 DOI: 10.1016/j.clindermatol.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2024]
Abstract
The integration of artificial intelligence (AI) in dermatology holds promise for enhancing clinical accuracy, enabling earlier detection of skin malignancies, suggesting potential management of skin lesions and eruptions, and promoting improved continuity of care. AI implementation in dermatology, however, raises several ethical concerns. This review explores the current benefits and challenges associated with AI integration, underscoring ethical considerations related to autonomy, informed consent, and privacy. We also examine the ways in which beneficence, nonmaleficence, and distributive justice may be impacted. Clarifying the role of AI, striking a balance between security and transparency, fostering open dialogue with our patients, collaborating with developers of AI, implementing educational initiatives for dermatologists and their patients, and participating in the establishment of regulatory guidelines are essential to navigating ethical and responsible AI incorporation into dermatology.
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Affiliation(s)
- Maggie Chen
- Department of Dermatology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Albert E Zhou
- Department of Dermatology, University of Conneticut School of Medicine, Farmington, Connecticut, USA
| | - Neelesh Jain
- Department of Dermatology, University of Conneticut School of Medicine, Farmington, Connecticut, USA
| | - Christian Gronbeck
- Department of Dermatology, University of Conneticut School of Medicine, Farmington, Connecticut, USA
| | - Hao Feng
- Department of Dermatology, University of Conneticut School of Medicine, Farmington, Connecticut, USA
| | - Jane M Grant-Kels
- Department of Dermatology, University of Conneticut School of Medicine, Farmington, Connecticut, USA; Department of Dermatology, University of Florida College of Medicine, Gainesville, Florida, USA.
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Malik FS, Yousaf MH, Sial HA, Viriri S. Exploring dermoscopic structures for melanoma lesions' classification. Front Big Data 2024; 7:1366312. [PMID: 38590699 PMCID: PMC10999676 DOI: 10.3389/fdata.2024.1366312] [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: 01/06/2024] [Accepted: 02/26/2024] [Indexed: 04/10/2024] Open
Abstract
Background Melanoma is one of the deadliest skin cancers that originate from melanocytes due to sun exposure, causing mutations. Early detection boosts the cure rate to 90%, but misclassification drops survival to 15-20%. Clinical variations challenge dermatologists in distinguishing benign nevi and melanomas. Current diagnostic methods, including visual analysis and dermoscopy, have limitations, emphasizing the need for Artificial Intelligence understanding in dermatology. Objectives In this paper, we aim to explore dermoscopic structures for the classification of melanoma lesions. The training of AI models faces a challenge known as brittleness, where small changes in input images impact the classification. A study explored AI vulnerability in discerning melanoma from benign lesions using features of size, color, and shape. Tests with artificial and natural variations revealed a notable decline in accuracy, emphasizing the necessity for additional information, such as dermoscopic structures. Methodology The study utilizes datasets with clinically marked dermoscopic images examined by expert clinicians. Transformers and CNN-based models are employed to classify these images based on dermoscopic structures. Classification results are validated using feature visualization. To assess model susceptibility to image variations, classifiers are evaluated on test sets with original, duplicated, and digitally modified images. Additionally, testing is done on ISIC 2016 images. The study focuses on three dermoscopic structures crucial for melanoma detection: Blue-white veil, dots/globules, and streaks. Results In evaluating model performance, adding convolutions to Vision Transformers proves highly effective for achieving up to 98% accuracy. CNN architectures like VGG-16 and DenseNet-121 reach 50-60% accuracy, performing best with features other than dermoscopic structures. Vision Transformers without convolutions exhibit reduced accuracy on diverse test sets, revealing their brittleness. OpenAI Clip, a pre-trained model, consistently performs well across various test sets. To address brittleness, a mitigation method involving extensive data augmentation during training and 23 transformed duplicates during test time, sustains accuracy. Conclusions This paper proposes a melanoma classification scheme utilizing three dermoscopic structures across Ph2 and Derm7pt datasets. The study addresses AI susceptibility to image variations. Despite a small dataset, future work suggests collecting more annotated datasets and automatic computation of dermoscopic structural features.
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Affiliation(s)
- Fiza Saeed Malik
- Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan
| | - Muhammad Haroon Yousaf
- Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan
- School of Computing, College of Science, Engineering and Technology, University of South Africa (UNISA), Pretoria, South Africa
| | | | - Serestina Viriri
- School of Computing, College of Science, Engineering and Technology, University of South Africa (UNISA), Pretoria, South Africa
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa
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Waseh S, Lee JB. Advances in melanoma: epidemiology, diagnosis, and prognosis. Front Med (Lausanne) 2023; 10:1268479. [PMID: 38076247 PMCID: PMC10703395 DOI: 10.3389/fmed.2023.1268479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/13/2023] [Indexed: 06/30/2024] Open
Abstract
Unraveling the multidimensional complexities of melanoma has required concerted efforts by dedicated community of researchers and clinicians battling against this deadly form of skin cancer. Remarkable advances have been made in the realm of epidemiology, classification, diagnosis, and therapy of melanoma. The treatment of advanced melanomas has entered the golden era as targeted personalized therapies have emerged that have significantly altered the mortality rate. A paradigm shift in the approach to melanoma classification, diagnosis, prognosis, and staging is underway, fueled by discoveries of genetic alterations in melanocytic neoplasms. A morphologic clinicopathologic classification of melanoma is expected to be replaced by a more precise molecular based one. As validated, convenient, and cost-effective molecular-based tests emerge, molecular diagnostics will play a greater role in the clinical and histologic diagnosis of melanoma. Artificial intelligence augmented clinical and histologic diagnosis of melanoma is expected to make the process more streamlined and efficient. A more accurate model of prognosis and staging of melanoma is emerging based on molecular understanding melanoma. This contribution summarizes the recent advances in melanoma epidemiology, classification, diagnosis, and prognosis.
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Affiliation(s)
- Shayan Waseh
- Department of Dermatology, Temple University Hospital, Philadelphia, PA, United States
| | - Jason B. Lee
- Department of Dermatology, Thomas Jefferson University, Philadelphia, PA, United States
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Tognetti L, Cartocci A, Żychowska M, Savarese I, Cinotti E, Pizzichetta MA, Moscarella E, Longo C, Farnetani F, Guida S, Paoli J, Lallas A, Tiodorovic D, Stanganelli I, Magi S, Dika E, Zalaudek I, Suppa M, Argenziano G, Pellacani G, Perrot JL, Miracapillo C, Rubegni G, Cevenini G, Rubegni P. A risk-scoring model for the differential diagnosis of lentigo maligna and other atypical pigmented facial lesions of the face: The facial iDScore. J Eur Acad Dermatol Venereol 2023; 37:2301-2310. [PMID: 37467376 DOI: 10.1111/jdv.19360] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Due to progressive ageing of the population, the incidence of facial lentigo maligna (LM) of the face is increasing. Many benign simulators of LM and LMM, known as atypical pigmented facial lesions (aPFLs-pigmented actinic keratosis, solar lentigo, seborrheic keratosis, seborrheic-lichenoid keratosis, atypical nevus) may be found on photodamaged skin. This generates many diagnostic issues and increases the number of biopsies, with a subsequent impact on aesthetic outcome and health insurance costs. OBJECTIVES Our aim was to develop a risk-scoring classifier-based algorithm to estimate the probability of an aPFL being malignant. A second aim was to compare its diagnostic accuracy with that of dermoscopists so as to define the advantages of using the model in patient management. MATERIALS AND METHODS A total of 154 dermatologists analysed 1111 aPFLs and their management in a teledermatology setting: They performed pattern analysis, gave an intuitive clinical diagnosis and proposed lesion management options (follow-up/reflectance confocal microscopy/biopsy). Each case was composed of a dermoscopic and/or clinical picture plus metadata (histology, age, sex, location, diameter). The risk-scoring classifier was developed and tested on this dataset and then validated on 86 additional aPFLs. RESULTS The facial Integrated Dermoscopic Score (iDScore) model consisted of seven dermoscopic variables and three objective parameters (diameter ≥ 8 mm, age ≥ 70 years, male sex); the score ranged from 0 to 16. In the testing set, the facial iDScore-aided diagnosis was more accurate (AUC = 0.79 [IC 95% 0.757-0.843]) than the intuitive diagnosis proposed by dermatologists (average of 43.5%). In the management study, the score model reduced the number of benign lesions sent for biopsies by 41.5% and increased the number of LM/LMM cases sent for reflectance confocal microscopy or biopsy instead of follow-up by 66%. CONCLUSIONS The facial iDScore can be proposed as a feasible tool for managing patients with aPFLs.
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Affiliation(s)
- Linda Tognetti
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
| | - Alessandra Cartocci
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Magdalena Żychowska
- Department of Dermatology, Institute of Medical Sciences, Medical College of Rzeszow University, Rzeszów, Poland
| | - Imma Savarese
- Soc Dermatologia Pistoia-Prato, USL Toscana Centro, Pistoia, Italy
| | - Elisa Cinotti
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
| | - Maria Antonietta Pizzichetta
- Dermatology Clinic, Ospedale di Trieste, Trieste, Italy
- Department of Medical Oncology, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy
| | - Elvira Moscarella
- Dermatology Unit, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Caterina Longo
- Centro Oncologico ad Alta Tecnologia Diagnostica, Azienda Unità Sanitaria Locale, IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | - Francesca Farnetani
- Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | - Stefania Guida
- Vita-Salute San Raffaele University, Milan, Italy
- Dermatology Clinic, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - John Paoli
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
- Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Aimilios Lallas
- First Department of Dermatology, Aristotle University, Thessaloniki, Greece
| | | | - Ignazio Stanganelli
- Skin Cancer Unit, Scientific Institute of Romagna for the Study of Cancer, IRCCS, IRST, Meldola, Italy
- Department of Dermatology, University of Parma, Parma, Italy
| | - Serena Magi
- Skin Cancer Unit, Scientific Institute of Romagna for the Study of Cancer, IRCCS, IRST, Meldola, Italy
| | - Emi Dika
- Dermatology Unit, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
- Dermatology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Iris Zalaudek
- Dermatology Clinic, Ospedale di Trieste, Trieste, Italy
| | - Mariano Suppa
- Department of Dermatology, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
- Groupe d'Imagerie Cutanée Non-Invasive, Société Française de Dermatologie, Paris, France
- Department of Dermatology, Institut Jules Bordet, Brussels, Belgium
| | | | - Giovanni Pellacani
- Department of Dermatology, Policlinico Umberto I, University of Rome La Sapienza, Rome, Italy
| | - Jean Luc Perrot
- Dermatology Unit, University Hospital of St-Etienne, Saint Etienne, France
| | - Chiara Miracapillo
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
| | - Giovanni Rubegni
- Department of Ophthalmology, University of Catania, Catania, Italy
| | - Gabriele Cevenini
- Bioengineering and Biomedical Data Science Lab, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Pietro Rubegni
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
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Nazari S, Garcia R. Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review. Life (Basel) 2023; 13:2123. [PMID: 38004263 PMCID: PMC10672549 DOI: 10.3390/life13112123] [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: 09/25/2023] [Revised: 10/21/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
Skin cancer has become increasingly common over the past decade, with melanoma being the most aggressive type. Hence, early detection of skin cancer and melanoma is essential in dermatology. Computational methods can be a valuable tool for assisting dermatologists in identifying skin cancer. Most research in machine learning for skin cancer detection has focused on dermoscopy images due to the existence of larger image datasets. However, general practitioners typically do not have access to a dermoscope and must rely on naked-eye examinations or standard clinical images. By using standard, off-the-shelf cameras to detect high-risk moles, machine learning has also proven to be an effective tool. The objective of this paper is to provide a comprehensive review of image-processing techniques for skin cancer detection using clinical images. In this study, we evaluate 51 state-of-the-art articles that have used machine learning methods to detect skin cancer over the past decade, focusing on clinical datasets. Even though several studies have been conducted in this field, there are still few publicly available clinical datasets with sufficient data that can be used as a benchmark, especially when compared to the existing dermoscopy databases. In addition, we observed that the available artifact removal approaches are not quite adequate in some cases and may also have a negative impact on the models. Moreover, the majority of the reviewed articles are working with single-lesion images and do not consider typical mole patterns and temporal changes in the lesions of each patient.
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Sauter D, Lodde G, Nensa F, Schadendorf D, Livingstone E, Kukuk M. Deep learning in computational dermatopathology of melanoma: A technical systematic literature review. Comput Biol Med 2023; 163:107083. [PMID: 37315382 DOI: 10.1016/j.compbiomed.2023.107083] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/10/2023] [Accepted: 05/27/2023] [Indexed: 06/16/2023]
Abstract
Deep learning (DL) has become one of the major approaches in computational dermatopathology, evidenced by a significant increase in this topic in the current literature. We aim to provide a structured and comprehensive overview of peer-reviewed publications on DL applied to dermatopathology focused on melanoma. In comparison to well-published DL methods on non-medical images (e.g., classification on ImageNet), this field of application comprises a specific set of challenges, such as staining artifacts, large gigapixel images, and various magnification levels. Thus, we are particularly interested in the pathology-specific technical state-of-the-art. We also aim to summarize the best performances achieved thus far with respect to accuracy, along with an overview of self-reported limitations. Accordingly, we conducted a systematic literature review of peer-reviewed journal and conference articles published between 2012 and 2022 in the databases ACM Digital Library, Embase, IEEE Xplore, PubMed, and Scopus, expanded by forward and backward searches to identify 495 potentially eligible studies. After screening for relevance and quality, a total of 54 studies were included. We qualitatively summarized and analyzed these studies from technical, problem-oriented, and task-oriented perspectives. Our findings suggest that the technical aspects of DL for histopathology in melanoma can be further improved. The DL methodology was adopted later in this field, and still lacks the wider adoption of DL methods already shown to be effective for other applications. We also discuss upcoming trends toward ImageNet-based feature extraction and larger models. While DL has achieved human-competitive accuracy in routine pathological tasks, its performance on advanced tasks is still inferior to wet-lab testing (for example). Finally, we discuss the challenges impeding the translation of DL methods to clinical practice and provide insight into future research directions.
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Affiliation(s)
- Daniel Sauter
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany.
| | - Georg Lodde
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | - Felix Nensa
- Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
| | | | - Markus Kukuk
- Department of Computer Science, Fachhochschule Dortmund, 44227 Dortmund, Germany
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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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Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2370190. [PMID: 35685142 PMCID: PMC9173896 DOI: 10.1155/2022/2370190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/18/2022] [Accepted: 05/09/2022] [Indexed: 11/21/2022]
Abstract
Melanoma is a kind of skin cancer caused by the irregular development of pigment-producing cells. Since melanoma detection efficiency is limited to different factors such as poor contrast among lesions and nearby skin regions, and visual resemblance among melanoma and non-melanoma lesions, intelligent computer-aided diagnosis (CAD) models are essential. Recently, computational intelligence (CI) and deep learning (DL) techniques are utilized for effective decision-making in the biomedical field. In addition, the fast-growing advancements in computer-aided surgeries and recent progress in molecular, cellular, and tissue engineering research have made CI an inevitable part of biomedical applications. In this view, the research work here develops a novel computational intelligence-based melanoma detection and classification technique using dermoscopic images (CIMDC-DIs). The proposed CIMDC-DI model encompasses different subprocesses. Primarily, bilateral filtering with fuzzy k-means (FKM) clustering-based image segmentation is applied as a preprocessing step. Besides, NasNet-based feature extractor with stochastic gradient descent is applied for feature extraction. Finally, the manta ray foraging optimization (MRFO) algorithm with a cascaded neural network (CNN) is exploited for the classification process. To ensure the potential efficiency of the CIMDC-DI technique, we conducted a wide-ranging simulation analysis, and the results reported its effectiveness over the existing recent algorithms with the maximum accuracy of 97.50%.
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