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BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm.

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Title: BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm.
Authors: Gottumukkala, V. S. S. P. Raju1 (AUTHOR) gvsspraju25@gmail.com, Kumaran, N.1 (AUTHOR), Sekhar, V. Chandra2 (AUTHOR)
Source: Expert Systems. Nov2022, Vol. 39 Issue 9, p1-16. 16p.
Subjects: Image processing equipment, Machine learning, Instructional systems, Deep learning, Artificial intelligence, Early detection of cancer
Abstract: Background: Skin lesion detection and classification (SLDC) is extremely important in the diagnosis of skin cancer and detection of melanoma cancer. As a result, the use of image processing equipment integrated with artificial intelligence can assist dermatologists in their decision‐making and examination. In addition, all deep learning (DL) structures consumes more time due to the large number of associated factors in filters and layers. Furthermore, if the architecture is insufficient to prototype the classification system, it must go through a lengthy retraining procedure. Material and method: Therefore, this article proposes a broad learning system (BLS) using incremental learning algorithm for the classification of non‐melanoma and melanoma skin lesions from dermoscopic images. Here after the proposed model is termed as BLSNet. Results: Experiments on ISIC 2019 and PH2 dataset indicate that proposed SLDC using BLSNet out‐perform the existing DL‐based SLDC models with an accuracy of 99.09% and F1‐score of 98.73%. Further, the overall execution time of proposed BLSNet is 0.93 s, which is superior as compared to the conventional approaches. Conclusion: Thus, the performance trade‐off between classification accuracy and execution time is achieved using proposed BLSNet model. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Image+processing+equipment%22">Image processing equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Early+detection+of+cancer%22">Early detection of cancer</searchLink>
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  Data: Background: Skin lesion detection and classification (SLDC) is extremely important in the diagnosis of skin cancer and detection of melanoma cancer. As a result, the use of image processing equipment integrated with artificial intelligence can assist dermatologists in their decision‐making and examination. In addition, all deep learning (DL) structures consumes more time due to the large number of associated factors in filters and layers. Furthermore, if the architecture is insufficient to prototype the classification system, it must go through a lengthy retraining procedure. Material and method: Therefore, this article proposes a broad learning system (BLS) using incremental learning algorithm for the classification of non‐melanoma and melanoma skin lesions from dermoscopic images. Here after the proposed model is termed as BLSNet. Results: Experiments on ISIC 2019 and PH2 dataset indicate that proposed SLDC using BLSNet out‐perform the existing DL‐based SLDC models with an accuracy of 99.09% and F1‐score of 98.73%. Further, the overall execution time of proposed BLSNet is 0.93 s, which is superior as compared to the conventional approaches. Conclusion: Thus, the performance trade‐off between classification accuracy and execution time is achieved using proposed BLSNet model. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/exsy.12938
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        Text: English
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Instructional systems
        Type: general
      – SubjectFull: Deep learning
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Early detection of cancer
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      – TitleFull: BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm.
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            NameFull: Gottumukkala, V. S. S. P. Raju
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            – D: 01
              M: 11
              Text: Nov2022
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              Y: 2022
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