Brain Tumor Classification 🧠
URL: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri
The purpose of this project is to get me familiar with the workflow of an Image Classification project and to exclude the importance of accuracy nor any kinds of metrics (in case you want to improve model performance, you can try to add more layers, more neurons in a layer or even apply transfer learning, etc.). I hope that this repo can help you in your first steps just like it did for me, good luck to you all ❤.
A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and 36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties.
Application of automated classification techniques using Machine Learning(ML) and Artificial Intelligence(AI)has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using ConvolutionNeural Network (CNN), Artificial Neural Network (ANN), and TransferLearning (TL) would be helpful to doctors all around the world.
- Dataset contains 3264 MRI images of 4 classes: glioma_tumor, meningioma_tumor, pituitary_tumor, no_tumor
- The mentioned images are divided into 2 folders: Training and Test. Training folder has 2870 images and Test folder has 394 images
