The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas
Skin cancer, and its less common form melanoma, is a disease affecting a wide variety of people. Since it is usually detected initially by visual inspection, it makes for a good candidate for the application of machine learning. With early detection being key to good outcomes, any method that can en...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Skin Cancer |
| Online Access: | http://dx.doi.org/10.1155/2022/2839162 |
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| _version_ | 1832563359766020096 |
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| author | Suboh Alkhushayni Du’a Al-zaleq Luwis Andradi Patrick Flynn |
| author_facet | Suboh Alkhushayni Du’a Al-zaleq Luwis Andradi Patrick Flynn |
| author_sort | Suboh Alkhushayni |
| collection | DOAJ |
| description | Skin cancer, and its less common form melanoma, is a disease affecting a wide variety of people. Since it is usually detected initially by visual inspection, it makes for a good candidate for the application of machine learning. With early detection being key to good outcomes, any method that can enhance the diagnostic accuracy of dermatologists and oncologists is of significant interest. When comparing different existing implementations of machine learning against public datasets and several we seek to create, we attempted to create a more accurate model that can be readily adapted to use in clinical settings. We tested combinations of models, including convolutional neural networks (CNNs), and various layers of data manipulation, such as the application of Gaussian functions and trimming of images to improve accuracy. We also created more traditional data models, including support vector classification, K-nearest neighbor, Naïve Bayes, random forest, and gradient boosting algorithms, and compared them to the CNN-based models we had created. Results had indicated that CNN-based algorithms significantly outperformed other data models we had created. Partial results of this work were presented at the CSET Presentations for Research Month at the Minnesota State University, Mankato. |
| format | Article |
| id | doaj-art-5a28ae2fc78e463b932058acac3eee45 |
| institution | Kabale University |
| issn | 2090-2913 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Skin Cancer |
| spelling | doaj-art-5a28ae2fc78e463b932058acac3eee452025-02-03T01:20:18ZengWileyJournal of Skin Cancer2090-29132022-01-01202210.1155/2022/2839162The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and MelanomasSuboh Alkhushayni0Du’a Al-zaleq1Luwis Andradi2Patrick Flynn3Minnesota State UniversityMinnesota State UniversityMinnesota State UniversityMinnesota State UniversitySkin cancer, and its less common form melanoma, is a disease affecting a wide variety of people. Since it is usually detected initially by visual inspection, it makes for a good candidate for the application of machine learning. With early detection being key to good outcomes, any method that can enhance the diagnostic accuracy of dermatologists and oncologists is of significant interest. When comparing different existing implementations of machine learning against public datasets and several we seek to create, we attempted to create a more accurate model that can be readily adapted to use in clinical settings. We tested combinations of models, including convolutional neural networks (CNNs), and various layers of data manipulation, such as the application of Gaussian functions and trimming of images to improve accuracy. We also created more traditional data models, including support vector classification, K-nearest neighbor, Naïve Bayes, random forest, and gradient boosting algorithms, and compared them to the CNN-based models we had created. Results had indicated that CNN-based algorithms significantly outperformed other data models we had created. Partial results of this work were presented at the CSET Presentations for Research Month at the Minnesota State University, Mankato.http://dx.doi.org/10.1155/2022/2839162 |
| spellingShingle | Suboh Alkhushayni Du’a Al-zaleq Luwis Andradi Patrick Flynn The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas Journal of Skin Cancer |
| title | The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas |
| title_full | The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas |
| title_fullStr | The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas |
| title_full_unstemmed | The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas |
| title_short | The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas |
| title_sort | application of differing machine learning algorithms and their related performance in detecting skin cancers and melanomas |
| url | http://dx.doi.org/10.1155/2022/2839162 |
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