Experimenting with Extreme Learning Machine for Biomedical Image Classification
Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has re...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/14/8558 |
| _version_ | 1827734009477595136 |
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| author | Francesco Mercaldo Luca Brunese Fabio Martinelli Antonella Santone Mario Cesarelli |
| author_facet | Francesco Mercaldo Luca Brunese Fabio Martinelli Antonella Santone Mario Cesarelli |
| author_sort | Francesco Mercaldo |
| collection | DOAJ |
| description | Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility of considering extreme learning machines for biomedical classification tasks. Binary and multiclass classification in four case studies are considered to demonstrate the effectiveness of extreme learning machine, considering the biomedical images acquired with the dermatoscope and with the blood cell microscope, showing that the extreme learning machine can be successfully applied for biomedical image classification. |
| first_indexed | 2024-03-11T01:19:29Z |
| format | Article |
| id | doaj.art-585a5fef28a6455a8e09af6f73eb6876 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:19:29Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-585a5fef28a6455a8e09af6f73eb68762023-11-18T18:14:47ZengMDPI AGApplied Sciences2076-34172023-07-011314855810.3390/app13148558Experimenting with Extreme Learning Machine for Biomedical Image ClassificationFrancesco Mercaldo0Luca Brunese1Fabio Martinelli2Antonella Santone3Mario Cesarelli4Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyInstitute for Informatics and Telematics, National Research Council of Italy, 56121 Pisa, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Engineering, University of Sannio, 82100 Benevento, ItalyCurrently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility of considering extreme learning machines for biomedical classification tasks. Binary and multiclass classification in four case studies are considered to demonstrate the effectiveness of extreme learning machine, considering the biomedical images acquired with the dermatoscope and with the blood cell microscope, showing that the extreme learning machine can be successfully applied for biomedical image classification.https://www.mdpi.com/2076-3417/13/14/8558extreme learning machinesbiomedical image classification |
| spellingShingle | Francesco Mercaldo Luca Brunese Fabio Martinelli Antonella Santone Mario Cesarelli Experimenting with Extreme Learning Machine for Biomedical Image Classification Applied Sciences extreme learning machines biomedical image classification |
| title | Experimenting with Extreme Learning Machine for Biomedical Image Classification |
| title_full | Experimenting with Extreme Learning Machine for Biomedical Image Classification |
| title_fullStr | Experimenting with Extreme Learning Machine for Biomedical Image Classification |
| title_full_unstemmed | Experimenting with Extreme Learning Machine for Biomedical Image Classification |
| title_short | Experimenting with Extreme Learning Machine for Biomedical Image Classification |
| title_sort | experimenting with extreme learning machine for biomedical image classification |
| topic | extreme learning machines biomedical image classification |
| url | https://www.mdpi.com/2076-3417/13/14/8558 |
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