Update README.md

README.md

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 # Brain tumor Detection by CT Scan
 
+Brain Tumor Detection Model 🧠
+This Brain Tumor Detection Model is a Convolutional Neural Network (CNN) trained to classify brain CT scan images into two categories:
+
+Healthy (No Tumor)
+Tumor Detected
+The model is designed to assist healthcare professionals by providing an automated solution for detecting brain tumors in CT scans. With high accuracy and balanced metrics, it is a reliable tool for preliminary screening and decision support.
+
+Model Performance
+
+Precision- 97%
+Recall	- 97%
+F1-Score - 97%
+Accuracy - 97%
+
+False Positives (Healthy misclassified as Tumor): 17
+False Negatives (Tumor misclassified as Healthy): 15
+
+Model Strengths
+High Accuracy: Achieves a test accuracy of 97%, making it reliable for tumor detection.
+Balanced Metrics: Precision and recall are well-balanced across both classes.
+Low False Negatives: The model minimizes missed tumor cases, crucial for medical applications.
+Efficient Architecture: Lightweight CNN architecture ensures quick inference times, suitable for real-time applications.
+
+Model Limitations
+
+No Multi-Class Support: The model supports binary classification (Healthy/Tumor); it cannot classify tumor subtypes.
+
 
 ```python
 # Example Code: Want To Quick try our model?just copy paste this code on colab