Magnetic Resonance Imaging (MRI) Brain Tumor Image Classification Based on Five Machine Learning Algorithms

التفاصيل البيبلوغرافية
العنوان: Magnetic Resonance Imaging (MRI) Brain Tumor Image Classification Based on Five Machine Learning Algorithms
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
الوصف
تدمد:27374092
27374106
DOI:10.37256/ccds.4220232740