Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things
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 s...
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| Vydané v: | Skin research and technology Ročník 29; číslo 11; s. e13524 - n/a |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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England
John Wiley & Sons, Inc
01.11.2023
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| ISSN: | 0909-752X, 1600-0846, 1600-0846 |
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| 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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| AbstractList | 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. 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. 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.INTRODUCTIONParticularly 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.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.METHODThis 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.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.RESULTSThe 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.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.CONCLUSIONIn 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. 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. 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. 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. 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. |
| Author | Faheem, Muhammad Akram, Arslan Jaffar, Muhammad Arfan Rashid, Javed Amin, Riaz ul |
| Author_xml | – sequence: 1 givenname: Arslan surname: Akram fullname: Akram, Arslan organization: MLC Research Lab – sequence: 2 givenname: Javed surname: Rashid fullname: Rashid, Javed organization: University of Okara – sequence: 3 givenname: Muhammad Arfan surname: Jaffar fullname: Jaffar, Muhammad Arfan organization: Superior University Lahore – sequence: 4 givenname: Muhammad orcidid: 0000-0003-4628-4486 surname: Faheem fullname: Faheem, Muhammad email: muhammad.faheem@uwasa.fi organization: University of Vaasa – sequence: 5 givenname: Riaz ul surname: Amin fullname: Amin, Riaz ul organization: University of Okara |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38009016$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.32604/cmc.2023.032005 10.3390/s19235072 10.1002/ett.3963 10.1007/978-3-031-18896-1_7 10.1007/978-981-10-6614-6_12 10.1049/htl2.12049 10.1111/coin.12458 10.1007/978-981-33-4367-2_6 10.3390/jimaging7040067 10.1117/12.2682553 10.3389/fphys.2023.1126780 10.3390/diagnostics11050811 10.1007/s10278‐021‐00552‐0 10.1016/j.jnca.2020.102873 10.3390/app12115714 10.1111/srt.13150 10.3390/biomedicines11061733 10.1016/j.dib.2023.108940 10.1109/CVPRW59228.2023.00454 10.1007/978-3-319-49655-9_57 10.1038/s41598-023-30930-3 10.1109/ICSPIS57063.2022.10002541 10.1007/s11042‐020‐09067‐2 10.1117/1.JMI.6.2.024001 10.1109/JTEHM.2023.3282104 10.1111/srt.12920 10.3390/s23073548 10.21595/jve.2022.22271 10.1109/TMI.2020.2972964 10.1016/j.eswa.2020.113742 10.1108/WJE‐09‐2020‐0456 10.1049/cit2.12261 10.1109/ICDCS48716.2020.243558 10.1038/sdata.2018.161 10.3390/diagnostics11081390 10.1093/eurpub/ckz216 10.1016/j.media.2021.102293 10.1016/j.cmpb.2019.105241 10.1016/j.compbiomed.2022.105545 10.32604/cmc.2023.035287 10.1186/s40537-023-00769-6 10.1016/j.neucom.2022.03.042 10.1016/j.engappai.2023.106445 10.1016/j.eswa.2015.04.034 10.1007/978-3-030-87193-2_20 10.1002/ijc.33588 10.1007/s11276‐021‐02713‐z 10.1016/j.asoc.2022.109906 10.1111/srt.12817 |
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| Keywords | melanoma skin cancer deep learning ResNet50 MRCNN ISIC-2020 Internet of Medical Things |
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| References_xml | – start-page: 206 year: 2021 end-page: 216 article-title: Boundary‐aware transformers for skin lesion segmentation – volume: 10 start-page: 105 issue: 1 year: 2023 article-title: Skin‐Net: a novel deep residual network for skin lesions classification using multilevel feature extraction and cross‐channel correlation with detection of outlier publication-title: J Big Data – start-page: 111 year: 2018 end-page: 119 article-title: Melanoma skin cancer detection using image processing – volume: 47 year: 2023 article-title: Datasets for training and validating a deep learning‐based system to detect microfossil fish teeth from slide images publication-title: Data in Brief – volume: 161 year: 2020 article-title: Skin lesion segmentation using fully convolutional networks: a comparative experimental study publication-title: Expert Syst Appl – volume: 42 start-page: 6578 issue: 19 year: 2015 end-page: 6585 article-title: MED‐NODE: A computer‐assisted melanoma diagnosis system using non‐dermoscopic images publication-title: Expert Syst Appl – volume: 23 start-page: 33 issue: 1 year: 2023 article-title: A novel thresholding for prediction analytics with machine learning techniques publication-title: Int J Comput Sci Netw Secur – start-page: 101 year: 2020 end-page: 104 article-title: Internet of Medical Things (IoMT)—an overview – volume: 19 start-page: 459 issue: 4 year: 2022 end-page: 466 article-title: Image splicing detection using discriminative robust local binary pattern and support vector machine publication-title: World J Eng – volume: 11 start-page: 1390 issue: 8 year: 2021 article-title: Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review publication-title: Diagnostics – start-page: 97 year: 2022 end-page: 102 article-title: DeepSkinNet: a deep learning model for skin cancer detection – volume: 491 start-page: 206 year: 2022 end-page: 216 article-title: CS‐AF: a cost‐sensitive multi‐classifier active fusion framework for skin lesion classification publication-title: Neurocomputing – volume: 149 start-page: 778 issue: 4 year: 2021 end-page: 789 article-title: Cancer statistics for the year 2020: an overview publication-title: Int J Cancer – volume: 15 start-page: 1 year: 2023 end-page: 9 article-title: Improving accuracy of convolutional neural network‐based skin lesion segmentation using group normalization and combined loss function publication-title: Int J Inf Technol – volume: 7 start-page: 67 issue: 4 year: 2021 article-title: Skin lesion segmentation using deep learning with auxiliary task publication-title: J Imaging – volume: 26 start-page: 413 issue: 3 year: 2020 end-page: 421 article-title: Ros‐NET: a deep convolutional neural network for automatic identification of rosacea lesions publication-title: Skin Res Technol – volume: 19 start-page: 5072 issue: 23 year: 2019 article-title: A multiobjective, lion mating optimization inspired routing protocol for wireless body area sensor network based healthcare applications publication-title: Sensors – volume: 14 start-page: 160 year: 2023 article-title: Fundus image classification using Inception V3 and ResNet‐50 for the early diagnostics of fundus diseases publication-title: Front Physiol – volume: 10 start-page: 1 year: 2023 end-page: 19 article-title: Mouth and oral disease classification using InceptionResNetV2 method publication-title: Multimed Tools Appl – volume: 75 start-page: 1863 issue: 1 year: 2023 end-page: 1881 article-title: Deep learning method to detect the road cracks and potholes for smart cities publication-title: Comput Mater Contin – volume: 8 start-page: 755 issue: 3 year: 2023 end-page: 769 article-title: D2PAM: epileptic seizures prediction using adversarial deep dual patch attention mechanism publication-title: CAAI Trans Intell Technol – volume: 11 start-page: 811 issue: 5 year: 2021 article-title: Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization publication-title: Diagnostics – volume: 186 year: 2020 article-title: Skin lesion segmentation using high‐resolution convolutional neural network publication-title: Comput Methods Programs Biomed – volume: 11 start-page: 341 year: 2023 end-page: 350 article-title: A Hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images publication-title: IEEE J Transl Eng Health Med – year: 2022 – volume: 35 start-page: 258 issue: 2 year: 2022 end-page: 280 article-title: Refined residual deep convolutional network for skin lesion classification publication-title: J Digit Imaging – start-page: 377 year: 2023 end-page: 382 – volume: 30 start-page: 1026 issue: 5 year: 2020 end-page: 1027 article-title: Cancer statistics: a comparison between world health organization (WHO) and global burden of disease (GBD) publication-title: Eur J Public Health – volume: 79 start-page: 24029 issue: 33 year: 2020 end-page: 24055 article-title: Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks publication-title: Multimed Tools Appl – year: 2019 – volume: 5 start-page: 1 issue: 1 year: 2018 end-page: 9 article-title: The HAM10000 dataset, a large collection of multi‐source dermatoscopic images of common pigmented skin lesions publication-title: Sci Data – volume: 29 start-page: 1507 issue: 4 year: 2023 end-page: 1521 article-title: Boosting and rectifying few‐shot learning prototype network for skin lesion classification based on the internet of medical things publication-title: Wireless Netw – volume: 133 year: 2023 article-title: Detection of Covid‐19 and other pneumonia cases from CT and X‐ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network publication-title: Appl Soft Comput – volume: 28 start-page: 571 issue: 4 year: 2022 end-page: 576 article-title: A deep learning approach to detect blood vessels in basal cell carcinoma publication-title: Skin Res Technol – volume: 146 year: 2022 article-title: NCRNet: neighborhood context refinement network for skin lesion segmentation publication-title: Comput Biol Med – volume: 10 start-page: 87 issue: 4 year: 2023 end-page: 98 article-title: Secure medical image transmission using deep neural network in e‐health applications publication-title: Healthc Technol Lett – start-page: 4314 year: 2023 end-page: 4324 article-title: An ensemble method with edge awareness for abnormally shaped nuclei segmentation – volume: 75 year: 2022 article-title: Ms RED: a novel multi‐scale residual encoding and decoding network for skin lesion segmentation publication-title: Med Image Anal – volume: 11 start-page: 1 issue: 1 year: 2023 end-page: 8 article-title: Machine learning based prediction of osteoporosis in postmenopausal women with clinical examined features: a quantitative clinical study publication-title: Health Sci Rep – volume: 6 issue: 2 year: 2019 article-title: Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons publication-title: J Med Imaging – volume: 12 start-page: 5714 issue: 11 year: 2022 article-title: Skin cancer disease detection using transfer learning technique publication-title: Appl Sci – volume: 24 start-page: 666 issue: 4 year: 2022 end-page: 678 article-title: A convolutional neural network method based on Adam optimizer with power‐exponential learning rate for bearing fault diagnosis publication-title: J Vibroengineering – volume: 4 issue: 3 year: 2022 article-title: An automatic helmet detection system using convolution neural network publication-title: Int J Adv Sci Innov – volume: 2 start-page: 55 year: 2021 end-page: 62 article-title: Skin cancer classification through transfer learning using ResNet‐50 – volume: 13 start-page: 6765 issue: 1 year: 2023 article-title: Assessment of experimental OpenCV tracking algorithms for ultrasound videos publication-title: Sci Rep – volume: 23 start-page: 3548 issue: 7 year: 2023 article-title: An IoMT‐based melanoma lesion segmentation using conditional generative adversarial networks publication-title: Sensors – volume: 173 year: 2021 article-title: IoT‐based telemedicine for disease prevention and health promotion: state‐of‐the‐art publication-title: J Netw Comput Appl – volume: 42 start-page: 22 issue: 20 year: 2012 end-page: 26 article-title: Automatic detection of melanoma skin cancer using texture analysis publication-title: Int J Comput Appl – volume: 123 year: 2023 article-title: Enhancing detection performance for robotic harvesting systems through RandAugment publication-title: Eng Appl Artif Intell – volume: 32 issue: 7 year: 2021 article-title: An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning publication-title: Trans Emerg Telecommun Technol – volume: 39 start-page: 2482 issue: 7 year: 2020 end-page: 2493 article-title: A mutual bootstrapping model for automated skin lesion segmentation and classification publication-title: IEEE Trans Med Imaging – volume: 38 start-page: 229 issue: 1 year: 2022 end-page: 248 article-title: Telemedicine virtual reality based skin image in children's dermatology medical system publication-title: Comput Intell – volume: 11 start-page: 1733 issue: 6 year: 2023 article-title: MSF‐Net: a lightweight multi‐scale feature fusion network for skin lesion segmentation publication-title: Biomedicines – start-page: 79 year: 2023 end-page: 91 – volume: 74 start-page: 1235 issue: 1 year: 2023 end-page: 1257 article-title: Real‐time multiple guava leaf disease detection from a single leaf using hybrid deep learning technique publication-title: Comput Mater Contin – volume: 27 start-page: 126 issue: 2 year: 2021 end-page: 137 article-title: Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images publication-title: Skin Res Technol – start-page: 468 year: 2017 end-page: 475 – ident: e_1_2_11_21_1 doi: 10.32604/cmc.2023.032005 – ident: e_1_2_11_12_1 doi: 10.3390/s19235072 – ident: e_1_2_11_38_1 doi: 10.1002/ett.3963 – ident: e_1_2_11_7_1 doi: 10.1007/978-3-031-18896-1_7 – ident: e_1_2_11_2_1 doi: 10.1007/978-981-10-6614-6_12 – ident: e_1_2_11_6_1 doi: 10.1049/htl2.12049 – ident: e_1_2_11_11_1 doi: 10.1111/coin.12458 – ident: e_1_2_11_26_1 doi: 10.1007/978-981-33-4367-2_6 – volume: 23 start-page: 33 issue: 1 year: 2023 ident: e_1_2_11_46_1 article-title: A novel thresholding for prediction analytics with machine learning techniques publication-title: Int J Comput Sci Netw Secur – ident: e_1_2_11_54_1 doi: 10.3390/jimaging7040067 – ident: e_1_2_11_45_1 doi: 10.1117/12.2682553 – ident: e_1_2_11_41_1 doi: 10.3389/fphys.2023.1126780 – ident: e_1_2_11_37_1 doi: 10.3390/diagnostics11050811 – ident: e_1_2_11_30_1 doi: 10.1007/s10278‐021‐00552‐0 – ident: e_1_2_11_9_1 doi: 10.1016/j.jnca.2020.102873 – ident: e_1_2_11_19_1 doi: 10.3390/app12115714 – ident: e_1_2_11_16_1 doi: 10.1111/srt.13150 – volume: 11 start-page: 1 issue: 1 year: 2023 ident: e_1_2_11_22_1 article-title: Machine learning based prediction of osteoporosis in postmenopausal women with clinical examined features: a quantitative clinical study publication-title: Health Sci Rep – ident: e_1_2_11_57_1 doi: 10.3390/biomedicines11061733 – ident: e_1_2_11_47_1 doi: 10.1016/j.dib.2023.108940 – ident: e_1_2_11_40_1 doi: 10.1109/CVPRW59228.2023.00454 – ident: e_1_2_11_28_1 doi: 10.1007/978-3-319-49655-9_57 – ident: e_1_2_11_44_1 doi: 10.1038/s41598-023-30930-3 – ident: e_1_2_11_25_1 doi: 10.1109/ICSPIS57063.2022.10002541 – ident: e_1_2_11_32_1 doi: 10.1007/s11042‐020‐09067‐2 – ident: e_1_2_11_36_1 doi: 10.1117/1.JMI.6.2.024001 – ident: e_1_2_11_8_1 doi: 10.1109/JTEHM.2023.3282104 – ident: e_1_2_11_23_1 doi: 10.1111/srt.12920 – ident: e_1_2_11_13_1 doi: 10.3390/s23073548 – volume: 4 issue: 3 year: 2022 ident: e_1_2_11_15_1 article-title: An automatic helmet detection system using convolution neural network publication-title: Int J Adv Sci Innov – ident: e_1_2_11_49_1 doi: 10.21595/jve.2022.22271 – ident: e_1_2_11_34_1 doi: 10.1109/TMI.2020.2972964 – ident: e_1_2_11_52_1 doi: 10.1016/j.eswa.2020.113742 – ident: e_1_2_11_18_1 doi: 10.1108/WJE‐09‐2020‐0456 – ident: e_1_2_11_10_1 doi: 10.1049/cit2.12261 – volume: 15 start-page: 1 year: 2023 ident: e_1_2_11_58_1 article-title: Improving accuracy of convolutional neural network‐based skin lesion segmentation using group normalization and combined loss function publication-title: Int J Inf Technol – ident: e_1_2_11_5_1 doi: 10.1109/ICDCS48716.2020.243558 – ident: e_1_2_11_33_1 doi: 10.1038/sdata.2018.161 – volume: 42 start-page: 22 issue: 20 year: 2012 ident: e_1_2_11_14_1 article-title: Automatic detection of melanoma skin cancer using texture analysis publication-title: Int J Comput Appl – ident: e_1_2_11_31_1 doi: 10.3390/diagnostics11081390 – ident: e_1_2_11_3_1 doi: 10.1093/eurpub/ckz216 – ident: e_1_2_11_55_1 doi: 10.1016/j.media.2021.102293 – ident: e_1_2_11_51_1 doi: 10.1016/j.cmpb.2019.105241 – ident: e_1_2_11_56_1 doi: 10.1016/j.compbiomed.2022.105545 – ident: e_1_2_11_24_1 doi: 10.32604/cmc.2023.035287 – ident: e_1_2_11_43_1 doi: 10.1186/s40537-023-00769-6 – ident: e_1_2_11_35_1 doi: 10.1016/j.neucom.2022.03.042 – ident: e_1_2_11_42_1 doi: 10.1016/j.engappai.2023.106445 – ident: e_1_2_11_27_1 doi: 10.1016/j.eswa.2015.04.034 – ident: e_1_2_11_53_1 doi: 10.1007/978-3-030-87193-2_20 – volume: 10 start-page: 1 year: 2023 ident: e_1_2_11_20_1 article-title: Mouth and oral disease classification using InceptionResNetV2 method publication-title: Multimed Tools Appl – ident: e_1_2_11_4_1 doi: 10.1002/ijc.33588 – ident: e_1_2_11_39_1 doi: 10.1007/s11276‐021‐02713‐z – ident: e_1_2_11_29_1 – ident: e_1_2_11_48_1 doi: 10.1016/j.asoc.2022.109906 – ident: e_1_2_11_50_1 – ident: e_1_2_11_17_1 doi: 10.1111/srt.12817 |
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Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy... Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and... Introduction Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy... |
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| SubjectTerms | Accuracy Artificial neural networks Best practice Classification Datasets Deep Learning Dermoscopy - methods Humans Image segmentation Internet Internet of Medical Things ISIC‐2020 Lesions Machine learning Medical imaging Melanoma - pathology melanoma skin cancer MRCNN Neural networks ResNet50 Semantic segmentation Skin cancer Skin diseases Skin Diseases - diagnostic imaging Skin lesions Skin Neoplasms - diagnostic imaging Skin Neoplasms - pathology |
| Title | Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things |
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