Update README.md

README.md

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 ---
 license: mit
+datasets:
+- Falah/Alzheimer_MRI
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+library_name: keras
+pipeline_tag: image-classification
+tags:
+- medical
 ---
+# Efficient-AD: An EfficientNetB0-based CNN Model for Alzheimer's Disease Detection
+
+Hey there! 👋 I am thrilled to introduce Efficient-AD, a cutting-edge Convolutional Neural Network (CNN) model designed specifically for detecting Alzheimer's disease using brain MRI scans. This innovative model is built upon the EfficientNetB0 architecture, showcasing remarkable accuracy and performance.
+
+## What Sets Efficient-AD Apart?
+
+Alzheimer's disease poses a significant challenge, impacting cognitive functions and necessitating early detection for effective intervention. Efficient-AD takes a giant leap in Alzheimer's detection, achieving an outstanding accuracy of 99.06%. This model is the result of meticulous research and optimization, addressing the limitations of existing CNN architectures.
+
+## Key Features:
+- **EfficientNetB0 Backbone:** Leveraging the power of EfficientNetB0, known for its exceptional balance of accuracy and efficiency.
+- **Deep Funnel Architecture:** We've fine-tuned the architecture with a deep funnel design, enhancing the model's capacity to understand complex patterns within MRI scans.
+- **Transfer Learning Magic:** Efficient-AD is pre-trained on ImageNet, providing a foundation for learning intricate features and fine-tuned for Alzheimer's disease detection.
+- **ReLU Activation Function:** Harnessing the benefits of Rectified Linear Unit (ReLU), ensuring faster convergence and improved gradient flow during training.
+
+## Performance Overview:
+
+Efficient-AD showcases superior performance, outperforming other state-of-the-art models like DenseNet121, NASNetMobile, and VGG16. It excels in accuracy, precision, recall, and F1 score, making it a frontrunner in Alzheimer's disease detection.
+
+## How to Use Efficient-AD:
+
+Efficient-AD is now available on [Hugging Face](https://huggingface.co/antrikxh/Efficient-AD). You can seamlessly integrate this model into your projects.
+
+# Environmental Impact
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** Tesla P-100
+- **Hours used:** 72 hours
+- **Cloud Provider:** Google Cloud Platform
+- **Compute Region:** europe-north1
+- **Carbon Emitted:** 3.78
+
+Feel free to explore and integrate Efficient-AD into your projects. Together, let's make strides in early Alzheimer's detection and contribute to improved healthcare outcomes. 🌟