CLASSIFICATION WITH MACHINE LEARNING METHODS FROM MULTI-SEQUENCE MR IMAGES OF CHILDHOOD POSTERIOR FOSSA TUMORS
Usak University Journal of Engineering Sciences(2024)
ERCİYES ÜNİVERSİTESİ
Abstract
Childhood brain tumors rank high among the leading causes of mortality, being the second most common type of cancer after leukemia. Abnormal structures in the brain are visualized using MRI techniques, which are the most commonly employed tools for distinguishing the neural structure of the human brain. However, identifying and diagnosing these abnormal structures can be a time-consuming and critical process. In this study, tumors in the Magnetic Resonance images of patients with Posterior Fossa tumors were segmented using two different image segmentation methods. Subsequently, numerical features were extracted from these tumors, and significant numerical features among tumor groups were determined using the Student's T-test; based on these features, tumor types were classified using machine learning algorithms. The study focused on the three most common types of Posterior Fossa tumors: Medulloblastoma, Ependymoma, and Pilocytic Astrocytoma, utilizing T2, Contrast-Enhanced T1, and ADC sequences. A total of forty-eight different numerical features were extracted from the segmented tumors and then acquired significant features were classified using five different machine learning algorithms. Among PA-MB, EM-MB and EM-PA tumor types, the average result of the most successful method in the T1 sequence was 86.93%, while it was 93.7% for the T2 sequence and 92.06% for the ADC sequence. Decision tree, SVM and Ensemble classifiers gave more successful results than others. As a result of the detailed examination, our study not only makes valuable contributions to the literature, but also has a promising structure in terms of its potential to help clinicians.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined