Update README.md

README.md

@@ -1,11 +1,95 @@
 ---
+library_name: monai
+tags:
+- classification
+- medical
+- neuroscience
+- alzheimer
 license: mit
-language:
-- en
 metrics:
 - accuracy
-library_name: keras
 pipeline_tag: image-classification
-tags:
-- medical
----
+datasets:
+- ADNI
+---
+---
+
+### Model Description
+A machine learning model for waste classification
+
+- **Developed by:** rootstrap
+- **Model type:** image-classifier
+- **License:** mit
+
+## Waste Classifier Model
+The aim is to build a model for MRI classification that identifies among the different classes:
+
+- Alzheimer's
+- Mild Cognitive Impairment
+- Control
+
+This machine learning model will help medical staff get a second opinion on whether a pacient MRI indicates the presence of Alzheimer's desease 
+
+
+The model was built using **Monai** MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging.
+
+It has two main design goals:
+
+To be approachable and rapidly productive
+To be also configurable.
+
+### Model Sources 
+
+- **Repository:** [https://github.com/rootstrap/MRI-classifier](https://github.com/rootstrap/MRI-classifier)
+
+## Uses
+
+At Rootstrap we classify waste. We found that people were struggled to classify correctly, 
+and then we end up not recycling most of the generated waste at the office, since if there were items in the wrong basket, 
+all the basket should not be classified.
+Because of this reason, we created an app to help people at our company to classify waste. 
+
+### Direct Use
+
+```bash
+model = nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=3)
+checkpoint = torch.load("86_acc_model.pth")
+model.load_state_dict(checkpoint)
+model.predict()
+
+```
+
+## Bias, Risks, and Limitations
+
+This model has an 86% of accuracy. Although it is an outstanding result, his means that 13% of times the prediction is wrong.
+This is why its important to understand that this tool is not a real medical opinion and can not be used as a final diagnosis by any means.
+This project does not aim to replace medical staff in diagnosing Alzheimer's desease, instead it is a tool to help them to get a quick and accurate second opinion. 
+
+## Training Details
+
+### Training Data
+
+The MRI data was gathered from the Alzheimer’s Desease Neuroimaging Initiative (ADNI) Database.
+Afterwards, the data was splitted into one folder for each class.
+
+Split into train/test 
+The data was splitted using the 60% for training, 20% for validation and 20% for testing
+
+The model has been trained to classify waste into 3 classes. 
+The dataset used for the training consisted of 2527 images:
+- 501 glass
+- 594 paper
+- 403 cardboard
+- 482 plastic
+- 410 metal
+- 137 trash
+
+
+### Training Procedure
+
+You can find the code for training at [train.ipynb](https://github.com/rootstrap/MRI-classifier/blob/main/train.ipynb)
+Training the model using DenseNet121 adapted for 3D images.
+
+## Evaluation and Results
+
+After 100 epochs, the model reached an accuracy of 85.76%