Skin lesion classification of dermoscopic images using machine learning and convolutional neural network
Abstract Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-22644-9 |
| _version_ | 1811257046960439296 |
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| author | Bhuvaneshwari Shetty Roshan Fernandes Anisha P. Rodrigues Rajeswari Chengoden Sweta Bhattacharya Kuruva Lakshmanna |
| author_facet | Bhuvaneshwari Shetty Roshan Fernandes Anisha P. Rodrigues Rajeswari Chengoden Sweta Bhattacharya Kuruva Lakshmanna |
| author_sort | Bhuvaneshwari Shetty |
| collection | DOAJ |
| description | Abstract Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners. |
| first_indexed | 2024-04-12T17:50:29Z |
| format | Article |
| id | doaj.art-f8a03bdcbca64b01888f04f602d5917e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T17:50:29Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f8a03bdcbca64b01888f04f602d5917e2022-12-22T03:22:31ZengNature PortfolioScientific Reports2045-23222022-10-0112111110.1038/s41598-022-22644-9Skin lesion classification of dermoscopic images using machine learning and convolutional neural networkBhuvaneshwari Shetty0Roshan Fernandes1Anisha P. Rodrigues2Rajeswari Chengoden3Sweta Bhattacharya4Kuruva Lakshmanna5Department of Computer Science and Engineering, Government Polytechnic for WomenDepartment of Computer Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University)Department of Computer Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University)School of Information Technology and Engineering, VITSchool of Information Technology and Engineering, VITSchool of Information Technology and Engineering, VITAbstract Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners.https://doi.org/10.1038/s41598-022-22644-9 |
| spellingShingle | Bhuvaneshwari Shetty Roshan Fernandes Anisha P. Rodrigues Rajeswari Chengoden Sweta Bhattacharya Kuruva Lakshmanna Skin lesion classification of dermoscopic images using machine learning and convolutional neural network Scientific Reports |
| title | Skin lesion classification of dermoscopic images using machine learning and convolutional neural network |
| title_full | Skin lesion classification of dermoscopic images using machine learning and convolutional neural network |
| title_fullStr | Skin lesion classification of dermoscopic images using machine learning and convolutional neural network |
| title_full_unstemmed | Skin lesion classification of dermoscopic images using machine learning and convolutional neural network |
| title_short | Skin lesion classification of dermoscopic images using machine learning and convolutional neural network |
| title_sort | skin lesion classification of dermoscopic images using machine learning and convolutional neural network |
| url | https://doi.org/10.1038/s41598-022-22644-9 |
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