Machine Learning project for the detection and classification of brain tumors by analyzing magnetic resonance images (MRI)
Brain tumors are considered one of the most aggressive diseases that can affect both adults and children. Brain tumors account for 80-90% of all primary central nervous system (CNS) tumors.
Each year, about 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is about 34% for men and 36% for women.
It is necessary to carry out a diagnosis and a price treatment to improve the life expectancy of patients. One of the best techniques for its detection is through magnetic resonance imaging. (MRI), where a large number of images are generated and examined by the radiologist.
Brain tumors are complex, there are many abnormalities in the size and location of brain tumors. This makes it difficult to fully understand the nature of the tumor.
Often in developing countries, the lack of trained physicians and the lack of knowledge about brain tumors make MRI reporting a tedious and time-consuming job.
This is where artificial intelligence can help doctors in this situation to make a more accurate diagnosis and in less time.
In this project a model is proposed that detects the presence of tumors and their classification using machine learning (ML). For this, we have worked with Deep Learning algorithms using Convolution Neural Network (CNN) and Transfer Learning (TL).
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