Machine learning and transfer learning techniques for accurate brain tumor classification
Brain tumors, resulting from uncontrolled and rapid cell growth, pose significant health risks if not treated early. Despite numerous advancements, accurate segmentation and classification remain challenging. This study leverages machine learning (ML) and transfer learning techniques to classify hea...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Clinical eHealth |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2588914124000091 |
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| _version_ | 1850043577137102848 |
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| author | Seyed Matin Malakouti Mohammad Bagher Menhaj Amir Abolfazl Suratgar |
| author_facet | Seyed Matin Malakouti Mohammad Bagher Menhaj Amir Abolfazl Suratgar |
| author_sort | Seyed Matin Malakouti |
| collection | DOAJ |
| description | Brain tumors, resulting from uncontrolled and rapid cell growth, pose significant health risks if not treated early. Despite numerous advancements, accurate segmentation and classification remain challenging. This study leverages machine learning (ML) and transfer learning techniques to classify healthy and sick individuals using numerical data and MRI images. We utilized 3762 MRI images alongside Light Gradient Boosting Machine (LightGBM), AdaBoost, gradient boosting, Random Forest, Quadratic Discriminant Analysis, Linear Discriminant Analysis, logistic regression, and transfer learning algorithms. Numerical data was processed with LightGBM, achieving an accuracy of 95.7 %. Transfer learning applied to image data using a modified GoogLeNet model further enhanced classification accuracy to 99.3 %. These results demonstrate the effectiveness of combining ML and transfer learning techniques for accurate brain tumor classification, addressing limitations of prior approaches and offering improved diagnostic reliability. All coding and model implementations were conducted on the Python platform. |
| format | Article |
| id | doaj-art-7ade14d9b80e457db9b7bf56df40d5b8 |
| institution | DOAJ |
| issn | 2588-9141 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Clinical eHealth |
| spelling | doaj-art-7ade14d9b80e457db9b7bf56df40d5b82025-08-20T02:55:12ZengKeAi Communications Co., Ltd.Clinical eHealth2588-91412024-12-01710611910.1016/j.ceh.2024.08.001Machine learning and transfer learning techniques for accurate brain tumor classificationSeyed Matin Malakouti0Mohammad Bagher Menhaj1Amir Abolfazl Suratgar2Distributed and Intelligent Optimization Research Laboratory, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IranDistributed and Intelligent Optimization Research Laboratory, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IranDistributed and Intelligent Optimization Research Laboratory, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IranBrain tumors, resulting from uncontrolled and rapid cell growth, pose significant health risks if not treated early. Despite numerous advancements, accurate segmentation and classification remain challenging. This study leverages machine learning (ML) and transfer learning techniques to classify healthy and sick individuals using numerical data and MRI images. We utilized 3762 MRI images alongside Light Gradient Boosting Machine (LightGBM), AdaBoost, gradient boosting, Random Forest, Quadratic Discriminant Analysis, Linear Discriminant Analysis, logistic regression, and transfer learning algorithms. Numerical data was processed with LightGBM, achieving an accuracy of 95.7 %. Transfer learning applied to image data using a modified GoogLeNet model further enhanced classification accuracy to 99.3 %. These results demonstrate the effectiveness of combining ML and transfer learning techniques for accurate brain tumor classification, addressing limitations of prior approaches and offering improved diagnostic reliability. All coding and model implementations were conducted on the Python platform.http://www.sciencedirect.com/science/article/pii/S2588914124000091Brain tumorMRI imagesNumerical dataLight gradient boosting machineTransfer learning |
| spellingShingle | Seyed Matin Malakouti Mohammad Bagher Menhaj Amir Abolfazl Suratgar Machine learning and transfer learning techniques for accurate brain tumor classification Clinical eHealth Brain tumor MRI images Numerical data Light gradient boosting machine Transfer learning |
| title | Machine learning and transfer learning techniques for accurate brain tumor classification |
| title_full | Machine learning and transfer learning techniques for accurate brain tumor classification |
| title_fullStr | Machine learning and transfer learning techniques for accurate brain tumor classification |
| title_full_unstemmed | Machine learning and transfer learning techniques for accurate brain tumor classification |
| title_short | Machine learning and transfer learning techniques for accurate brain tumor classification |
| title_sort | machine learning and transfer learning techniques for accurate brain tumor classification |
| topic | Brain tumor MRI images Numerical data Light gradient boosting machine Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S2588914124000091 |
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