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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Introduction:

This study introduces SkinLiTE, a lightweight supervised contrastive learning model tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the challenge of complexities and imbalances inherent in skin lesion datasets.

Methods:

The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions.

Results:

SkinLiTE demonstrated superior performance across various metrics, including accuracy, AUC, and F1 scores, particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities.

Conclusion:

This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-06-29

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References

  1. CassidyB. KendrickC. BrodzickiA. Jaworek-KorjakowskaJ. YapM.H. Analysis of the ISIC image datasets: Usage, benchmarks and recommendations.Med. Image Anal.20227510230510.1016/j.media.2021.10230534852988
     [Google Scholar]
  2. MohantyA. SutherlandA. BezbradicaM. JavidniaH. Skin disease analysis with limited data in particular rosacea: A review and recommended framework.IEEE Access202210390453906810.1109/ACCESS.2022.3165574
     [Google Scholar]
  3. KumarY. KoulA. SinglaR. IjazM.F. Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda.J. Ambient Intell. Humaniz. Comput.202210.1007/s12652‑021‑03612‑z35039756
     [Google Scholar]
  4. VatiwutipongP. VachmanusS. NorasetT. TuarobS. Artificial intelligence in cosmetic dermatology: A systematic literature review.IEEE Access202311714077142510.1109/ACCESS.2023.3295001
     [Google Scholar]
  5. LiX. ZhaoX. MaH. XieB. Image analysis and diagnosis of skin diseases - A review.Curr. Med. Imaging202319319924210.2174/157340561866622051611460535578858
     [Google Scholar]
  6. GoyalB. KaurB. DanielE. A survey on machine learning based medical assistive systems in current oncological sciences.Curr. Med. Imaging202218544545910.2174/157340561766621021715444633596810
     [Google Scholar]
  7. Huérfano-MaldonadoY. MoraM. VilchesK. Hernández-GarcíaR. GutiérrezR. VeraM. A comprehensive review of extreme learning machine on medical imaging.Neurocomputing202355612661810.1016/j.neucom.2023.126618
     [Google Scholar]
  8. MangotraH. SrivastavaS. JaiswalG. RaniR. SharmaA. Hyperspectral imaging for early diagnosis of diseases: A review.Expert Syst.2023408e1331110.1111/exsy.13311
     [Google Scholar]
  9. SinghM. SinghM. DeD. HandaS. MahajanR. ChatterjeeD. Towards diagnosis of autoimmune blistering skin diseases using deep neural network.Arch. Comput. Methods Eng.20233063529355710.1007/s11831‑023‑09910‑3
     [Google Scholar]
  10. HosnyK.M. ElshouraD. MohamedE.R. VrochidouE. PapakostasG.A. Deep learning and optimization-based methods for skin lesions segmentation: A review.IEEE Access202311854678548810.1109/ACCESS.2023.3303961
     [Google Scholar]
  11. JiangH. DiaoZ. ShiT. ZhouY. WangF. HuW. ZhuX. LuoS. TongG. YaoY.D. A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation.Comput. Biol. Med.202315710672610.1016/j.compbiomed.2023.10672636924732
     [Google Scholar]
  12. NoronhaS.S. MehtaM.A. GargD. KotechaK. AbrahamA. Deep learning-based dermatological condition detection: A systematic review with recent methods, datasets, challenges, and future directions.IEEE Access20231114034814038110.1109/ACCESS.2023.3339635
     [Google Scholar]
  13. PainuliD. BhardwajS. köseU. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review.Comput. Biol. Med.202214610558010.1016/j.compbiomed.2022.10558035551012
     [Google Scholar]
  14. RaiH.M. Cancer detection and segmentation using machine learning and deep learning techniques: A review.Multimedia Tools Appl.2023839270012703510.1007/s11042‑023‑16520‑5
     [Google Scholar]
  15. NawazM. UvaliyevA. BibiK. WeiH. AbaxiS.M.D. MasoodA. ShiP. HoH.P. YuanW. Unraveling the complexity of optical coherence tomography image segmentation using machine and deep learning techniques: A review.Comput. Med. Imaging Graph.202310810226910.1016/j.compmedimag.2023.10226937487362
     [Google Scholar]
  16. IbrahimA. MohamedH.K. MaherA. ZhangB. A survey on human cancer categorization based on deep learning.Front. Artif. Intell.2022588474910.3389/frai.2022.88474935832207
     [Google Scholar]
  17. WangW.C. AhnE. FengD. KimJ. A review of predictive and contrastive self-supervised learning for medical images.Machine Intelligence Research202320448351310.1007/s11633‑022‑1406‑4
     [Google Scholar]
  18. KhoslaP. Supervised contrastive learning.arXiv20202004.11362.
     [Google Scholar]
  19. AlDeraS. A. Ben OthmanM. T. A model for classification and diagnosis of skin disease using machine learning and image processing techniques.Int. J. Adv. Comput. Sci. Appl.202213525225910.14569/IJACSA.2022.0130531
     [Google Scholar]
  20. HatemM.Q. Skin lesion classification system using a K-nearest neighbor algorithm.Vis. Comput. Ind. Biomed. Art202251710.1186/s42492‑022‑00103‑635229199
     [Google Scholar]
  21. SaghirU. HasanM. Skin cancer detection and classification based on differential analyzer algorithm.Multimedia Tools Appl.20238226411294115710.1007/s11042‑023‑14409‑x
     [Google Scholar]
  22. MustafaS. JaffarA. IqbalM.W. AbubakarA. AlshahraniA.S. AlghamdiA. Hybrid color texture features classification through ANN for melanoma.Intelligent Automation and Soft Computing2023352e02954910.32604/iasc.2023.029549
     [Google Scholar]
  23. HuongL. H. KhangN. H. QuynhL. ThangL. CanhD. M. SangH. P. A proposed approach for monkeypox classification.Int. J. Adv. Comput. Sci. Appl.202314864365110.14569/IJACSA.2023.0140871
     [Google Scholar]
  24. MunishKhanna SinghL.K. GargH. A novel approach for human diseases prediction using nature inspired computing & machine learning approach.Multimedia Tools Appl.2023836177731780910.1007/s11042‑023‑16236‑6
     [Google Scholar]
  25. MeenaK. VeniN.N.K. DeepapriyaB.S. VardhiniP.A.H. KalyaniB.J.D. SharmilaL. A novel method for prediction of skin disease through supervised classification techniques.Soft Comput.20222619105271053310.1007/s00500‑022‑07435‑8
     [Google Scholar]
  26. ZouQ. ChengJ. LiangZ. Automatic diagnosis of melanoma based on efficientnet and patch strategy.Int. J. Comput. Intell. Syst.20231618710.1007/s44196‑023‑00246‑1
     [Google Scholar]
  27. SpolaôrN. LeeH.D. MendesA.I. NogueiraC.V. ParmezanA.R.S. TakakiW.S.R. CoyC.S.R. WuF.C. Fonseca-PintoR. Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets.Multimedia Tools Appl.2023839273052732910.1007/s11042‑023‑16529‑w
     [Google Scholar]
  28. PatelM. B. Multi class skin diseases classification based on dermoscopic skin images using deep learning.Int. J. Next-Gen. Comput.202213215116110.47164/ijngc.v13i2.480
     [Google Scholar]
  29. ShaheenH. SinghM.P. Multiclass skin cancer classification using particle swarm optimization and convolutional neural network with information security.J. Electron. Imaging202232404210210.1117/1.JEI.32.4.042102
     [Google Scholar]
  30. AlghiethM. Skin disease detection for kids at school using deep learning techniques.Int. J. Online Biomed. Eng.2022181011412810.3991/ijoe.v18i10.31879
     [Google Scholar]
  31. SivakumarM.S. LeoL.M. GurumekalaT. SindhuV. PriyadharshiniA.S. Deep learning in skin lesion analysis for malignant melanoma cancer identification.Multimedia Tools Appl.2023836178331785310.1007/s11042‑023‑16273‑1
     [Google Scholar]
  32. AlrusainiO. A. Deep learning models for the detection of monkeypox skin lesion on digital skin images.Int. J. Adv. Comput. Sci. Appl.202314163764410.14569/IJACSA.2023.0140170
     [Google Scholar]
  33. TripathiA. SinghA.K. SinghA. ChoudharyA. PareekK. MishraK.K. Analyzing skin disease using XCNN (extended convolutional neural network).Int. J. Softw. Sci. Comput. Intell.202214113010.4018/IJSSCI.309708
     [Google Scholar]
  34. BalaD. HossainM.S. HossainM.A. AbdullahM.I. RahmanM.M. ManavalanB. GuN. IslamM.S. HuangZ. MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification.Neural Netw.202316175777510.1016/j.neunet.2023.02.02236848828
     [Google Scholar]
  35. WeiM. WuQ. JiH. WangJ. LyuT. LiuJ. ZhaoL. A skin disease classification model based on densenet and convnext fusion.Electronics202312243810.3390/electronics12020438
     [Google Scholar]
  36. El GannourO. HamidaS. LamalemY. CherradiB. SalehS. RaihaniA. Enhancing skin diseases classification through dual ensemble learning and pre-trained CNNs.Int. J. Adv. Comput. Sci. Appl.202314643644510.14569/IJACSA.2023.0140647
     [Google Scholar]
  37. AboulmiraA. HrimechH. LachgarM. Comparative study of multiple CNN models for classification of 23 skin diseases.Int. J. Online Biomed. Eng.2022181112714210.3991/ijoe.v18i11.32517
     [Google Scholar]
  38. AnandV. GuptaS. KoundalD. NayakS.R. NayakJ. VimalS. Multi-class skin disease classification using transfer learning model.Int. J. Artif. Intell. Tools2022312225002910.1142/S0218213022500294
     [Google Scholar]
  39. AnandV. GuptaS. NayakS.R. KoundalD. PrakashD. VermaK.D. An automated deep learning models for classification of skin disease using Dermoscopy images: A comprehensive study.Multimedia Tools Appl.20228126373793740110.1007/s11042‑021‑11628‑y
     [Google Scholar]
  40. AnandV. GuptaS. KoundalD. MahajanS. Kant PanditA. ZaguiaA. Deep learning based automated diagnosis of skin diseases using dermoscopy.Comput. Mater. Continua20227123145316010.32604/cmc.2022.022788
     [Google Scholar]
  41. NathS. Das GuptaS. SahaS. Deep learning-based common skin disease image classification.J. Intell. Fuzzy Syst.20234457483749910.3233/JIFS‑222773
     [Google Scholar]
  42. AroraG. DubeyA.K. JafferyZ.A. RochaA. A comparative study of fourteen deep learning networks for multi skin lesion classification (MSLC) on unbalanced data.Neural Comput. Appl.202335117989801510.1007/s00521‑022‑06922‑1
     [Google Scholar]
  43. Al-TuwaijariJ.M. YousirN.T. AlhammadN.A.M. MostafaS. Deep residual learning image recognition model for skin cancer disease detection and classification.Acta Inform. Prag.2023121193110.18267/j.aip.189
     [Google Scholar]
  44. TaşarB. SkinCancerNet: Automated classification of skin lesion using deep transfer learning method.TS Trait. Signal202340128529510.18280/ts.400128
     [Google Scholar]
  45. MagdyA. HusseinH. Abdel-KaderR.F. SalamK.A.E. Performance enhancement of skin cancer classification using computer vision.IEEE Access202311721207213310.1109/ACCESS.2023.3294974
     [Google Scholar]
  46. Priyanka PramilaR. SubhashiniR. Automated skin lesion detection and classification using fused deep convolutional neural network on dermoscopic images.Comput. Intell.20233961073108710.1111/coin.12590
     [Google Scholar]
  47. ArshedM.A. MumtazS. IbrahimM. AhmedS. TahirM. ShafiM. Multi-class skin cancer classification using vision transformer networks and convolutional neural network-based pre-trained models.Information202314741510.3390/info14070415
     [Google Scholar]
  48. RaghavendraP.V.S.P. CharithaC. BegumK.G. PrasathV.B.S. Deep learning–based skin lesion multi-class classification with global average pooling improvement.J. Digit. Imaging20233652227224810.1007/s10278‑023‑00862‑537407845
     [Google Scholar]
  49. Mathina KaniM.A.J. ParvathyM.S. Maajitha BanuS. Abdul KareemM.S. Classification of skin lesion images using modified Inception V3 model with transfer learning and augmentation techniques.J. Intell. Fuzzy Syst.20234434627464110.3233/JIFS‑221386
     [Google Scholar]
  50. AlorainiM. Two-stream convolutional networks for skin cancer classification.Multimed Tools Appl202483307413075310.1007/s11042‑023‑16758‑z
     [Google Scholar]
  51. RiazL. QadirH.M. AliG. AliM. RazaM.A. JurcutA.D. AliJ. A comprehensive joint learning system to detect skin cancer.IEEE Access202311794347944410.1109/ACCESS.2023.3297644
     [Google Scholar]
  52. KaurR. KaurN. Ti-FCNet: Triple fused convolutional neural network-based automated skin lesion classification.Multimedia Tools Appl.20238311325253255110.1007/s11042‑023‑16594‑1
     [Google Scholar]
  53. LiH. ZhangP. WeiZ. QianT. TangY. HuK. HuangX. XiaX. ZhangY. ChengH. YuF. ZhangW. DanK. LiuX. YeS. HeG. JiangX. LiuL. FanY. SongT. ZhouG. WangZ. ZhangD. LvJ. Deep skin diseases diagnostic system with dual-channel image and extracted text.Front. Artif. Intell.20236121362010.3389/frai.2023.121362037928449
     [Google Scholar]
  54. LiS. LiX. XuX. ChengK.T. Dynamic subcluster-aware network for few-shot skin disease classification.IEEE Trans. Neural Netw. Learn. Syst.2023PP11210.1109/TNNLS.2023.333676538090872
     [Google Scholar]
  55. ReddyD.A. RoyS. KumarS. TripathiR. Enhanced U-Net segmentation with ensemble convolutional neural network for automated skin disease classification.Knowl. Inf. Syst.202365104111415610.1007/s10115‑023‑01865‑y
     [Google Scholar]
  56. JosphineleelaR. Raja RaoP.B.V. shaikhA. SudhakarK. A multi-stage faster RCNN-based isplinception for skin disease classification using novel optimization.J. Digit. Imaging20233652210222610.1007/s10278‑023‑00848‑337322306
     [Google Scholar]
  57. SreekalaK. RajkumarN. SugumarR. SagarK.V.D. ShobaraniR. KrishnamoorthyK.P. SainiA.K. PalivelaH. YeshitlaA. Skin diseases classification using hybrid AI based localization approach.Comput. Intell. Neurosci.202220221710.1155/2022/613849036072725
     [Google Scholar]
  58. HeX. WangY. ZhaoS. ChenX. Joint segmentation and classification of skin lesions via a multi-task learning convolutional neural network.Expert Syst. Appl.202323012017410.1016/j.eswa.2023.120174
     [Google Scholar]
  59. AlamM.J. MohammadM.S. HossainM.A.F. ShowmikI.A. RaihanM.S. AhmedS. MahmudT.I. S 2 C-DeLeNet: A parameter transfer based segmentation-classification integration for detecting skin cancer lesions from dermoscopic images.Comput. Biol. Med.202215010614810.1016/j.compbiomed.2022.10614836252363
     [Google Scholar]
  60. DongC. DaiD. ZhangY. ZhangC. LiZ. XuS. Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification.Comput. Biol. Med.202315210632110.1016/j.compbiomed.2022.10632136463792
     [Google Scholar]
  61. YadavA.K. MehtaR. KumarV. MedikonduN.R. An optimized boosting framework for skin lesion segmentation and classification.Multimedia Tools Appl.20238318538755389610.1007/s11042‑023‑17042‑w
     [Google Scholar]
  62. DumanE. TolanZ. Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis.Int. J. Imaging Syst. Technol.20233341293130510.1002/ima.22872
     [Google Scholar]
  63. KalaivaniA. KarpagavalliS. Advanced domain adaptation for skin disease segmentation and classification using bootstrapping of fine-tuned deep learner.Multimedia Tools Appl.20238312353553537010.1007/s11042‑023‑17004‑2
     [Google Scholar]
  64. RamamurthyK. MuthuswamyA. MathimariappanN. KathiresanG.S. A novel two‐staged network for skin disease detection using atrous residual convolutional networks.Concurr. Comput.20233526e783410.1002/cpe.7834
     [Google Scholar]
  65. AlsahafiY.S. KassemM.A. HosnyK.M. Skin-Net: A novel deep residual network for skin lesions classification using multilevel feature extraction and cross-channel correlation with detection of outlier.J. Big Data202310110510.1186/s40537‑023‑00769‑6
     [Google Scholar]
  66. RaoG.M. RameshD. GantelaP. SrinivasK. A hybrid deep learning strategy for image based automated prognosis of skin disease.Soft Comput.202310.1007/s00500‑023‑08007‑0
     [Google Scholar]
  67. ManojS.O. AbiramiK.R. VictorA. AryaM. Automatic detection and categorization of skin lesions for early diagnosis of skin cancer using YOLO-v3 - DCNN architecture.Image Anal. Stereol.202342210111710.5566/ias.2773
     [Google Scholar]
  68. HaoS. ZhangL. JiangY. WangJ. JiZ. ZhaoL. GanchevI. ConvNeXt-ST-AFF: A novel skin disease classification model based on fusion of convnext and swin transformer.IEEE Access20231111746011747310.1109/ACCESS.2023.3324042
     [Google Scholar]
  69. KalyaniK. A AlthubitiS. Altaf AhmedM. Laxmi LydiaE. KadryS. HanN. NamY. Arithmetic optimization with ensemble deep transfer learning based燤elanoma classification.Comput. Mater. Continua202375114916410.32604/cmc.2023.033005
     [Google Scholar]
  70. SeejaR.D. GeethaA. Effective melanoma classification using inter neighbour mean order interleaved pattern on dermoscopy images.Multimedia Tools Appl.2023839274812750510.1007/s11042‑023‑16632‑y
     [Google Scholar]
  71. Abdulrahman AlbraikanA. NemriN. Abdullah AlkhonainiM. Mustafa HilalA. YaseenI. MotwakelA. Automated deep learning based melanoma detection and classification using biomedical dermoscopic images.Comput. Mater. Continua20237422443245910.32604/cmc.2023.026379
     [Google Scholar]
  72. MeswalH. KumarD. GuptaA. RoyS. A weighted ensemble transfer learning approach for melanoma classification from skin lesion images.Multimedia Tools Appl.20238311336153363710.1007/s11042‑023‑16783‑y
     [Google Scholar]
  73. AsifS. ZhaoM. TangF. ZhuY. ZhaoB. Metaheuristics optimization-based ensemble of deep neural networks for Mpox disease detection.Neural Netw.202316734235910.1016/j.neunet.2023.08.03537673024
     [Google Scholar]
  74. AlorainiM. An effective human monkeypox classification using vision transformer.Int. J. Imaging Syst. Technol.202310.1002/ima.22944
     [Google Scholar]
  75. RenG. Monkeypox disease detection with pretrained deep learning models.Inf. Technol. Control202352228829610.5755/j01.itc.52.2.32803
     [Google Scholar]
  76. AhsanM.M. UddinM.R. AliM.S. IslamM.K. FarjanaM. SakibA.N. MominK.A. LunaS.A. Deep transfer learning approaches for Monkeypox disease diagnosis.Expert Syst. Appl.202321611948310.1016/j.eswa.2022.11948336624785
     [Google Scholar]
  77. BianX. PanH. ZhangK. LiP. LiJ. ChenC. Skin lesion image classification method based on extension theory and deep learning.Multimedia Tools Appl.20228112163891640910.1007/s11042‑022‑12376‑3
     [Google Scholar]
  78. RadhikaV. ChandanaB.S. MSCDNet-based multi-class classification of skin cancer using dermoscopy imagesPeerJ Comput Sci20239e152010.7717/peerj‑cs.1520
     [Google Scholar]
  79. GhoshA. JanaN.D. DasS. MallipeddiR. Two-phase evolutionary convolutional neural network architecture search for medical image classification.IEEE Access20231111528011530510.1109/ACCESS.2023.3323705
     [Google Scholar]
  80. RajeshwariJ. SughasinyM. Modified filter based feature selection technique for dermatology dataset using beetle swarm optimization.ICST Trans. Scalable Inf. Syst.2022102e7810.4108/eetsis.vi.1998
     [Google Scholar]
  81. MostafaR.R. KhedrA.M. Al AghbariZ. AfyouniI. KamelI. AhmedN. An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets.Knowl. Base. Syst.202428311121810.1016/j.knosys.2023.111218
     [Google Scholar]
  82. ZhouJ. WuZ. JiangZ. HuangK. GuoK. ZhaoS. Background selection schema on deep learning-based classification of dermatological disease.Comput. Biol. Med.202214910596610.1016/j.compbiomed.2022.10596636029748
     [Google Scholar]
  83. JainA. RaoA.C.S. JainP.K. AbrahamA. Multi-type skin diseases classification using OP-DNN based feature extraction approach.Multimedia Tools Appl.20228156451647610.1007/s11042‑021‑11823‑x35035267
     [Google Scholar]
  84. LiuN. RejeeshM.R. SundararajV. GunasundariB. ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for classification of skin cancer.Expert Syst. Appl.202323212071910.1016/j.eswa.2023.12071937362255
     [Google Scholar]
  85. VidhyalakshmiA.M. KanchanaM. Skin cancer classification using improved transfer learning model-based random forest classifier and golden search optimization.Int. J. Imaging Syst. Technol.202310.1002/ima.22971
     [Google Scholar]
  86. RenithG. SenthilselviA. An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model.Int. J. Imaging Syst. Technol.20233361957197210.1002/ima.22932
     [Google Scholar]
  87. DesaleR.P. PatilP.S. An efficient multi-class classification of skin cancer using optimized vision transformer.Med. Biol. Eng. Comput.202310.1007/s11517‑023‑02969‑x37996627
     [Google Scholar]
  88. Pablo Villa-PulgarinJ. Alberto Ruales-TorresA. Arias-Garz髇D. Alejandro Bravo-OrtizM. Brayan Arteaga-ArteagaH. Mora-RubioA. Alejandro Alzate-GrisalesJ. Mercado-RuizE. HassaballahM. Orozco-AriasS. Cardona-MoralesO. Tabares-SotoR. Optimized convolutional neural network models for skin lesion classification.Comput. Mater. Continua20227022131214810.32604/cmc.2022.019529
     [Google Scholar]
  89. AnandV. GuptaS. KoundalD. SinghK. Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images.Expert Syst. Appl.202321311923010.1016/j.eswa.2022.119230
     [Google Scholar]
  90. AdepuA.K. SahayamS. JayaramanU. ArramrajuR. Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data.Comput. Biol. Med.202315410657110.1016/j.compbiomed.2023.10657136709518
     [Google Scholar]
  91. SalehR.E. ChantafS. Nait-aliA. Identification of facial skin diseases from face phenotypes using FSDNet in uncontrolled environment.Mach. Vis. Appl.20223322210.1007/s00138‑021‑01259‑6
     [Google Scholar]
  92. BozkurtF. Skin lesion classification on dermatoscopic images using effective data augmentation and pre-trained deep learning approach.Multimedia Tools Appl.20238212189851900310.1007/s11042‑022‑14095‑1
     [Google Scholar]
  93. PokhrelK. SaninC. SakibM.K.H. IslamM.R. SzczerbickiE. Improved skin disease classification with mask R-CNN and augmented dataset.Cybern. Syst.202311510.1080/01969722.2023.2296254
     [Google Scholar]
  94. HolmesK. SharmaP. FernandesS. Facial skin disease prediction using StarGAN v2 and transfer learning.Intell. Decision Technol.2023171556610.3233/IDT‑228046
     [Google Scholar]
  95. RezkE. EltorkiM. El-DakhakhniW. Interpretable skin cancer classification based on incremental domain knowledge learning.J. Healthc. Inform. Res.202371598310.1007/s41666‑023‑00127‑436910915
     [Google Scholar]
  96. HosnyK.M. SaidW. ElmezainM. KassemM.A. Explainable deep inherent learning for multi-classes skin lesion classification.Appl. Soft Comput.202415911162410.1016/j.asoc.2024.111624
     [Google Scholar]
  97. HoangV.D. VoX.T. JoK.H. Categorical weighting domination for imbalanced classification with skin cancer in intelligent healthcare systems.IEEE Access20231110517010518110.1109/ACCESS.2023.3319087
     [Google Scholar]
  98. YuL. WangY. ZhouL. WuJ. WangZ. Residual neural network‐assisted one‐class classification algorithm for melanoma recognition with imbalanced data.Comput. Intell.20233961004102110.1111/coin.12578
     [Google Scholar]
  99. ChenK. LeiW. ZhaoS. ZhengW-S. WangR. WangR. PCCT: Progressive class-center triplet loss for imbalanced medical image classification.IEEE J. Biomed. Health Inform.20232742026203610.1109/JBHI.2023.324013637022228
     [Google Scholar]
  100. OkuboyejoD.A. OlugbaraO.O. Classification of skin lesions using weighted majority voting ensemble deep learning.Algorithms2022151244310.3390/a15120443
     [Google Scholar]
  101. VidhyalakshmiA.M. KanchanaM. Classification of skin disease using a novel hybrid flash butterfly optimization from dermoscopic images.Neural Comput. Appl.202310.1007/s00521‑023‑09011‑z
     [Google Scholar]
  102. HuangZ. WuJ. WangT. LiZ. IoannouA. Class-specific distribution alignment for semi-supervised medical image classification.Comput. Biol. Med.202316410728010.1016/j.compbiomed.2023.10728037517324
     [Google Scholar]
  103. ZhuangJ.X. CaiJ. ZhangJ. ZhengW. WangR. Class attention to regions of lesion for imbalanced medical image recognition.Neurocomputing202355512657710.1016/j.neucom.2023.126577
     [Google Scholar]
  104. YaoP. ShenS. XuM. LiuP. ZhangF. XingJ. ShaoP. KaffenbergerB. XuR.X. Single model deep learning on imbalanced small datasets for skin lesion classification.IEEE Trans. Med. Imaging20224151242125410.1109/TMI.2021.313668234928791
     [Google Scholar]
  105. AlshawiS. A. MusawiG. Skin cancer image detection and classification by CNN based ensemble learning.Int. J. Adv. Comput. Sci. Appl.202314571071710.14569/IJACSA.2023.0140575
     [Google Scholar]
  106. YueG. WeiP. ZhouT. JiangQ. YanW. WangT. Toward multicenter skin lesion classification using deep neural network with adaptively weighted balance loss.IEEE Trans. Med. Imaging202342111913110.1109/TMI.2022.320464636063522
     [Google Scholar]
  107. OmerogluA.N. MohammedH.M.A. OralE.A. AydinS. A novel soft attention-based multi-modal deep learning framework for multi-label skin lesion classification.Eng. Appl. Artif. Intell.202312010589710.1016/j.engappai.2023.105897
     [Google Scholar]
  108. ZhangR. WangL. ChengS. SongS. MLP-based classification of COVID-19 and skin diseases.Expert Syst. Appl.202322812038910.1016/j.eswa.2023.12038937193247
     [Google Scholar]
  109. DesaleR.P. PatilP.S. An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification.Imaging Sci. J.202311510.1080/13682199.2023.2229018
     [Google Scholar]
  110. BaoQ. HanH. HuangL. MuzahidA.A.M. A convolutional neural network based on soft attention mechanism and multi-scale fusion for skin cancer classification.Int. J. Pattern Recognit. Artif. Intell.20233714235602410.1142/S0218001423560244
     [Google Scholar]
  111. ToH.D. NguyenH.G. LeH.T.T. LeH.M. QuanT.T. MetaAttention model: A new approach for skin lesion diagnosis using AB features and attention mechanism.Biomed. Phys. Eng. Express20239404500810.1088/2057‑1976/acd1f037137289
     [Google Scholar]
  112. SuratiS. TrivediH. ShrimaliB. BhattC. Travieso-GonzálezC.M. An enhanced diagnosis of monkeypox disease using deep learning and a novel attention model senet on diversified dataset.Multimodal Technol. Interact.2023787510.3390/mti7080075
     [Google Scholar]
  113. DamineniD.H. SekharamantryP.K. BaduguR. An adaptable model for medical image classification using the streamlined attention mechanism.Int. J. Online Biomed. Eng.202319169311010.3991/ijoe.v19i16.44461
     [Google Scholar]
  114. AshwathV.A. SikhaO.K. BenitezR. TS-CNN: A three-tier self-interpretable cnn for multi-region medical image classification.IEEE Access202311784027841810.1109/ACCESS.2023.3299850
     [Google Scholar]
  115. RenY. XuW. MaoY. WuY. FuB. ThanhD.N.H. Few‐shot learning for dermatological conditions with lesion area aware swin transformer.Int. J. Imaging Syst. Technol.20233351549156010.1002/ima.22891
     [Google Scholar]
  116. ZhangD.Z. LiA.L. WuW.D. YuL. KangX.J. HuoX.Z. CR-conformer: A fusion network for clinical skin lesion classification.Med. Biol. Eng. Comput.202310.1007/s11517‑023‑02904‑037653185
     [Google Scholar]
  117. ShaikT. TaoX. HigginsN. LiL. GururajanR. ZhouX. AcharyaU.R. Remote patient monitoring using artificial intelligence: Current state, applications, and challenges.Wiley Interdiscip. Rev. Data Min. Knowl. Discov.2023132e148510.1002/widm.1485
     [Google Scholar]
  118. OztelI. Yolcu OztelG. SahinV.H. Deep learning‐based skin diseases classification using smartphones.Adv. Intell. Syst.2023512230021110.1002/aisy.202300211
     [Google Scholar]
  119. YangY. XieF. ZhangH. WangJ. LiuJ. ZhangY. DingH. Skin lesion classification based on two-modal images using a multi-scale fully-shared fusion network.Comput. Methods Programs Biomed.202322910731510.1016/j.cmpb.2022.10731536586177
     [Google Scholar]
  120. ManzoorK. A lightweight approach for skin lesion detection through optimal features fusion.CMC-Comput. Mater. Continua20227011617163010.32604/cmc.2022.018621
     [Google Scholar]
  121. VivekanandaG.N. AlmuftiS.M. SureshC. SamsudeenS. DevarajanM.V. SrikanthR. JayashreeS. Retracing-efficient IoT model for identifying the skin-related tags using automatic lumen detection.Intell. Data Anal.20232716118010.3233/IDA‑237442
     [Google Scholar]
  122. ShahinM. ChenF.F. HosseinzadehA. Khodadadi KoodianiH. ShahinA. Ali NafiO. A smartphone-based application for an early skin disease prognosis: Towards a lean healthcare system via computer-based vision.Adv. Eng. Inform.20235710203610.1016/j.aei.2023.102036
     [Google Scholar]
  123. Kumar KA. SatheeshaT.Y. SalvadorB.B.L. MithileyshS. AhmedS.T. Augmented intelligence enabled deep neural networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards.Microprocess. Microsyst.20239710475510.1016/j.micpro.2023.104755
     [Google Scholar]
  124. ShiY. LiX. ChenS. Skin lesion intelligent diagnosis in edge computing networks: An FCL approach.ACM Trans. Intell. Syst. Technol.202314412210.1145/3595186
     [Google Scholar]
  125. GuptaA. BhagatM. JainV. Blockchain-enabled healthcare monitoring system for early Monkeypox detection.J. Supercomput.20237914156751569910.1007/s11227‑023‑05288‑y37359326
     [Google Scholar]
  126. HossenM.N. PanneerselvamV. KoundalD. AhmedK. BuiF.M. IbrahimS.M. Federated machine learning for detection of skin diseases and enhancement of internet of medical things (IoMT) security.IEEE J. Biomed. Health Inform.202327283584110.1109/JBHI.2022.314928835133971
     [Google Scholar]
  127. HendrycksD. MuN. CubukE.D. ZophB. GilmerJ. LakshminarayananB AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.arXiv: 1912.02781,2019
     [Google Scholar]
  128. ZhangH. CisseM Mixup: Beyond empirical risk minimization.arxiv: 1710.09412,2018
     [Google Scholar]
  129. YunS. HanD. ChunS. OhS.J. YooY. ChoeJ. CutMix: Regularization strategy to train strong classifiers with localizable features.2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 27 October 2019 - 02 November 2019, pp. 6022-6031.10.1109/ICCV.2019.00612
     [Google Scholar]
  130. RotembergV. KurtanskyN. Betz-StableinB. CafferyL. ChousakosE. CodellaN. CombaliaM. DuszaS. GuiteraP. GutmanD. HalpernA. HelbaB. KittlerH. KoseK. LangerS. LioprysK. MalvehyJ. MusthaqS. NandaJ. ReiterO. ShihG. StratigosA. TschandlP. WeberJ. SoyerH.P. A patient-centric dataset of images and metadata for identifying melanomas using clinical context.Sci. Data2021813410.1038/s41597‑021‑00815‑z33510154
     [Google Scholar]
  131. CodellaN.C.F. Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 04-07 April 2018, pp. 168-172.10.1109/ISBI.2018.8363547
     [Google Scholar]
  132. TschandlP. RosendahlC. KittlerH. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.Sci. Data20185118016110.1038/sdata.2018.16130106392
     [Google Scholar]
  133. CombaliaM. BCN20000: Dermoscopic lesions in the wild.arXiv:1908.02288,2019
     [Google Scholar]
  134. SinghP. SizikovaE. CirroneJ CASS: Cross architectural self-supervision for medical image analysis.arXiv:2206.04170,2022
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056313837240612065845
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SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images
Curr. Med. Imaging 20, e15734056313837 (2024); https://doi.org/10.2174/0115734056313837240612065845
/content/journals/cmir/10.2174/0115734056313837240612065845
/content/journals/cmir/10.2174/0115734056313837240612065845
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  • Article Type:     Research Article
Keyword(s): AI; Dermoscopic images; Skin condition; Skin disease classification; Skin lesions; Supervised contrastive learning

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