alsani-ipe / Brain-Tumor-Classification-with-custom-Neural-Network

Brain Tumors are complex. There are a lot of abnormalities in the sizes and location of the brain tumor(s). This makes it really difficult for complete understanding of the nature of the tumor. Also, a professional Neurosurgeon is required for MRI analysis

Title: Brain Tumor Classification with custom Neural Network

▶️ Introduction and brief introduction of data

Introduction

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 and36 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.

Summary

Brain Tumors are complex. There are a lot of abnormalities in the sizes and location of the brain tumor(s). This makes it really difficult for complete understanding of the nature of the tumor. Also, a professional Neurosurgeon is required for MRI analysis. Often times in developing countries the lack of skillful doctors and lack of knowledge about tumors makes it really challenging and time-consuming to generate reports from MRI’. So an automated system on Cloud can solve this problem.

Read More About Dataset: Click

The key folders in the dataset include:

1. GLIOMA TUMOR
2. MENINGIOMA TUMOR
3. NO TUMOR
4. PITUITARY TUMOR

Remote source:https://github.com/sartajbhuvaji/brain-tumor-classification-dataset The folder contains MRI data. The images are already split into Training and Testing folders. Each folder has more four subfolders. These folders have MRIs of respective tumor classes.

Conclusion

Please feel free to ask in the comment section if you have any confusion or questions.

Here are some of the contributions I've made on Kaggle:

1. Pie Charts in Python
2. Scatter plots with Plotly Express
3. X-ray Image Classification using Transfer Learning
4. Flowers Classification by Using VGG16 Model 🎉🎉
5. Car Brand Prediction's by Using ResNet50 Model
6. Image Preprocessing-Morpological Analysis & Kernel
7. Image Similarity Index (SSIM analysis )
8. Image Preprocessing- Image Transformation & OpenCV