Magnetic Resonance Imaging (MRI) Brain Tumor Image Classification Based on Five Machine Learning Algorithms
العنوان: | Magnetic Resonance Imaging (MRI) Brain Tumor Image Classification Based on Five Machine Learning Algorithms |
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المؤلفون: | null Song Jiang, null Yuan Gu, null Ela Kumar |
المصدر: | Cloud Computing and Data Science. :122-133 |
بيانات النشر: | Universal Wiser Publisher Pte. Ltd, 2023. |
سنة النشر: | 2023 |
مصطلحات موضوعية: | Psychiatry and Mental health |
الوصف: | With the emergence of new technologies, vast amounts of data have become pervasive in various aspects of social life, including public transportation, community services, and scientific research. As the population ages, healthcare has become increasingly crucial, and reducing the public burdens, especially in hospitals, has become an urgent issue. For instance, manually managing vast electronic medical files, such as MRI images, based on their types is practically impossible. However, accurate classification is fundamental and critical for subsequent tasks, such as diagnosis. In this article, we utilized machine learning techniques to classify MRI brain tumor images. We employed a range of machine learning models, including k-Nearest Neighbors (k-NN), decision tree, Support Vector Machine (SVM), logistic regression, and Stochastic Gradient Descent (SGD). The performance of each model type was measured by True Skill Statistics (TSS), based on the results obtained from the confusion matrix. The results showed that k-NN works most efficiently among all those classification models. However, due to the constraints of limited running time and computational power, further investigation of the models and parameter optimization are necessary for future work. |
تدمد: | 2737-4092 2737-4106 |
DOI: | 10.37256/ccds.4220232740 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::21da307fd743ac76b0a822f4bd38f0de https://doi.org/10.37256/ccds.4220232740 |
رقم الانضمام: | edsair.doi...........21da307fd743ac76b0a822f4bd38f0de |
قاعدة البيانات: | OpenAIRE |
تدمد: | 27374092 27374106 |
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DOI: | 10.37256/ccds.4220232740 |