Update README.md

README.md

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-This model, MalariaGuard, is designed to predict malaria cases in Africa using data from the World Health Organization.
+## BrainGuard: Brain Tumor Detection Model
 
 ### Model Description
 
-MalariaGuard is a machine learning model developed to predict malaria cases in Africa. It uses historical data from the World Health Organization to make accurate predictions, potentially aiding in resource allocation and prevention strategies.
+BrainGuard is a machine learning model developed to detect brain tumors from MRI scans. Leveraging neural network architecture, it processes MRI images to assist in early diagnosis, potentially supporting medical professionals in identifying cases that require further investigation.
 
 - **Developed by:** Alok Pandey
-- **Model type:** Neural Network
-- **Language(s) (NLP):** Not applicable (uses numerical data)
+- **Model type:** Neural Network (CNN)
+- **Language(s) (NLP):** Not applicable (uses image data)
 - **License:** MIT
-- **Finetuned from model :** Developed from scratch
-
+- **Finetuned from model:** Developed from scratch
 
 ### Direct Use
 
-This model can be used by health organizations, governments, and researchers to predict malaria cases in African countries. It can assist in planning prevention strategies, allocating resources, and preparing healthcare systems for potential outbreaks.
+This model is intended for use by healthcare organizations, researchers, and medical professionals to aid in the detection of brain tumors in MRI scans. It serves as a supportive tool for diagnosis, helping to streamline the review of large volumes of scans and potentially accelerating early detection.
 
 ### Out-of-Scope Use
 
-This model should not be used as the sole basis for medical decisions or to replace professional medical advice. It is a predictive tool and should be used in conjunction with other data sources and expert knowledge.
+This model is not a substitute for professional medical diagnosis and should not be used as the sole basis for treatment decisions. It is designed as an assistive tool and should always be used alongside expert evaluation and additional diagnostic tests.
 
 ## Bias, Risks, and Limitations
 
-- The model's predictions are based on historical data and may not account for sudden changes in environmental factors, healthcare policies, or unforeseen events.
-- The accuracy of predictions may vary across different regions of Africa due to potential differences in data quality or coverage.
-- Users should be aware that the model's high accuracy (98%) on the test set may not necessarily translate to real-world performance with the same level of accuracy.
+- The model’s accuracy may vary based on the diversity and quality of MRI scans provided. It is trained on specific imaging datasets and may have reduced performance on data from different sources or imaging equipment.
+- Predictions are based on patterns in the training data and may not account for rare or atypical tumor presentations.
+- While the model has high accuracy on the test set (95%), real-world accuracy may vary due to differences in clinical environments.
 
 ### Recommendations
 
 Users should:
-- Regularly update the model with the most recent data available.
-- Use the model's predictions as part of a broader decision-making process, not as the sole input.
+- Use the model’s predictions in conjunction with comprehensive medical evaluation and diagnostic testing.
+- Regularly update the model with diverse and representative MRI scan data.
 - Be aware of the model's limitations and potential biases when interpreting results.
 
 ### Training Data
 
-The model was trained on malaria case data from the World Health Organization, focusing on African countries. 
-
+The model was trained on a dataset of brain MRI scans that included labeled data indicating the presence or absence of brain tumors.
 
 #### Training Hyperparameters
 
@@ -45,15 +43,15 @@ The model was trained on malaria case data from the World Health Organization, f
 
 #### Testing Data
 
-The model was tested on a held-out portion of the World Health Organization's malaria case data for Africa.
+The model was validated on a separate portion of the MRI dataset to evaluate its performance on unseen cases.
 
 #### Metrics
 
-- **Accuracy:** 98%
+- **Accuracy:** 95%
 
 ### Model Architecture and Objective
 
-The model uses a neural network architecture implemented in Keras. Its objective is to predict malaria cases based on historical data.
+The model employs a Convolutional Neural Network (CNN) architecture implemented in Keras. Its objective is to detect the presence of brain tumors in MRI images.
 
 #### Software