A new approach to diagnosing prostate cancer through magnetic resonance imaging
| Autor: | Min Tang, Yi Huan, Xia Zhe, Longchao Li, Li Zhang, Xiaoling Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
GrowCut algorithm
Computer science 020209 energy Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 01 natural sciences 010305 fluids & plasmas Prostate cancer Ensemble learning 0103 physical sciences 0202 electrical engineering electronic engineering information engineering medicine Segmentation medicine.diagnostic_test business.industry General Engineering Zernik feature extraction Cancer Pattern recognition Magnetic resonance imaging medicine.disease Engineering (General). Civil engineering (General) Support vector machine ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence TA1-2040 business |
| Zdroj: | Alexandria Engineering Journal, Vol 60, Iss 1, Pp 897-904 (2021) |
| ISSN: | 1110-0168 |
| Popis: | This paper combines improved GrowCut and Zernik feature extraction and ensemble learning techniques such as KNN, SVM, and MLP algorithms to prostate cancer detection and lesion segmentation in MRI. We use improved GrowCut algorithm for segmentation of the suspected cancer area and the combination of machine learning algorithms such as KNN, SVM, and MLP in the ensemble learning system to detect prostate cancer. We found that the accuracy of this method, which is a combination of several methods, improved by about 20% compared to other methods. Other metrics such as precision, recall, and error of proposed method have been improved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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