SUBMISSIONS

SUBMISSION DETAIL

Hdr Seluk NOAY
 


Keywords:



BRAIN TUMOR CLASSIFICATION BY USING DEEP LEARNING
 
Brain tumor is considered one of the most aggressive diseases in children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System (CNS) tumors. Approximately 11,700 people are diagnosed with brain tumors each year. The 5-year survival rate for people with cancerous brain or CNS tumors is approximately 34 percent for men and 36 percent for women. A deep learning model that can automatically classify brain tumors from MRI images can significantly contribute to science and humanity. By accurately identifying and classifying brain tumors, doctors and radiologists can make more informed decisions about diagnosis, treatment plans, and monitoring disease progression. A deep learning model could lead to better patient outcomes and save lives. Additionally, automating the classification process using deep learning eliminates the potential for human error and bias. With the help of this model, healthcare professionals can increase diagnostic accuracy and obtain more reliable results by reducing the possibility of misinterpretation. Developing a robust deep-learning model for brain tumor classification will also facilitate neuroscience research and accelerate scientific discoveries. By automating the analysis of large volumes of MRI data, researchers can gain insight into tumor characteristics, patterns, and potential correlations with other health conditions. This type of deep-learning study could open new ways to study brain tumors and their progression, ultimately leading to the development of more effective treatments. A deep learning model that automatically classifies brain tumors from MRI images is critical. It can revolutionize medical diagnoses, improve patient care, reduce errors, and provide valuable information for further research. In this study, the automatic classification of brain tumors was carried out with deep convolutional neural networks, which are accepted as the most successful and popular deep learning methods and were trained and tested with brain MRI images, and significant accuracy rates and results were achieved. ORCID NO: 0000-0001-9105-508X

Anahtar Kelimeler: CNN, Transfer Learning, Deep Learning, Brain Tumor