Update README.md

README.md

@@ -22,52 +22,76 @@ model-index:
         metrics:
           - name: Accuracy
             type: accuracy
-            value: 0.85
+            value: 0.73
 ---
 
 # Skin Cancer Detection Model
 
-## Overview
-This model was created as part of a final project for an AI bootcamp. It is a **skin cancer detection** model trained to classify skin lesions from dermatoscopic images using the **HAM10000 dataset**. The model is capable of predicting **seven different types of skin lesions**, each corresponding to various forms of skin cancer and other skin conditions.
+This AI model is designed to detect various types of skin cancer from dermatoscopic images. It uses the HAM10000 dataset and is capable of classifying skin lesions into seven categories:
 
-The model has been trained using a **Convolutional Neural Network (CNN)** with **TensorFlow** and **Keras**. The goal of this project is to help in early detection of skin cancer by classifying images into seven distinct categories, which could assist healthcare professionals in diagnosis.
+1. **akiec** - Actinic Keratoses and Intraepithelial Carcinoma
+2. **bcc** - Basal Cell Carcinoma
+3. **bkl** - Benign Keratosis
+4. **df** - Dermatofibroma
+5. **nv** - Melanocytic Nevus
+6. **vasc** - Vascular Lesions
+7. **mel** - Melanoma
 
-## Model Architecture
-The model utilizes a CNN architecture fine-tuned for image classification tasks. Below is a brief description of the architecture:
-- **Input size**: 224x224 RGB images
-- **Base architecture**: Pretrained CNN (e.g., ResNet, VGG)
-- **Output layer**: 7 softmax units, each corresponding to one of the skin lesion categories
+## Model Overview
 
-## Model Performance
-The model was trained on the HAM10000 dataset and achieved an accuracy of **85%** on the validation set. Further improvements could be made by additional fine-tuning and hyperparameter optimization.
+The model is built using TensorFlow and Keras. It is a convolutional neural network (CNN) trained on the HAM10000 dataset, which contains over 10,000 images of skin lesions. This model can be used for automated skin cancer screening and classification, which can help assist dermatologists in diagnosing patients.
 
-## Datasets
-The model was trained using the **HAM10000 dataset**, which consists of over 10,000 dermatoscopic images of skin lesions. The dataset includes seven types of lesions, described as follows:
+### Features:
+- **Image classification** for seven types of skin cancer.
+- **Accuracy**: The model achieved an accuracy of 73% on the validation set.
+- Built with **TensorFlow** and **Keras**.
+- Open-sourced under the **MIT license**.
 
-| Label | Full Name | Description |
-|-------|------------|-------------|
-| **akiec** | Actinic Keratoses and Intraepithelial Carcinoma | A type of skin lesion that can develop into squamous cell carcinoma if left untreated. |
-| **bcc**  | Basal Cell Carcinoma | A common form of skin cancer that rarely metastasizes. |
-| **bkl**  | Benign Keratosis | Non-cancerous skin lesions like seborrheic keratosis. |
-| **df**   | Dermatofibroma | A benign skin lesion usually found on the lower legs. |
-| **nv**   | Melanocytic Nevus | Commonly known as a mole, usually benign but can develop into melanoma. |
-| **vasc** | Vascular Lesions | Skin lesions that involve blood vessels, like angiomas. |
-| **mel**  | Melanoma | The most dangerous form of skin cancer, often caused by UV radiation exposure. |
+## Dataset
 
-### Gradio Demo
+The HAM10000 ("Human Against Machine with 10000 training images") dataset is a collection of dermatoscopic images used to train this model. The dataset contains images of seven different classes of benign and malignant skin lesions.
 
-You can try out the **skin cancer detection model** using the interactive demo hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/syaha/skin-cancer-detection-gradio/app.py).
+### Dataset Classes:
+- **akiec**: Actinic keratoses and carcinoma
+- **bcc**: Basal cell carcinoma
+- **bkl**: Benign keratosis
+- **df**: Dermatofibroma
+- **nv**: Melanocytic nevus
+- **vasc**: Vascular lesions
+- **mel**: Melanoma
 
-## Usage
+More information on the dataset can be found [here](https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000).
 
-To use this model for inference, you can load it using TensorFlow:
+## How to Use the Model
+
+You can load the model directly in your Python environment and use it to classify new images. Here's an example of how to use the model:
 
 ```python
 from tensorflow.keras.models import load_model
+import tensorflow as tf
+import numpy as np
 
 # Load the model
-model = load_model("path_to_model.h5")
+model = load_model('path_to_your_model.h5')
+
+# Preprocess the image
+def preprocess_image(image_path):
+    img = tf.image.decode_image(tf.io.read_file(image_path), channels=3)
+    img = tf.image.resize(img, [224, 224])
+    img = np.expand_dims(img, axis=0)
+    img = img / 255.0  # Normalize the image
+    return img
+
+# Predict the class of the image
+def predict(image_path):
+    image = preprocess_image(image_path)
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction, axis=1)[0]
+    
+    class_names = ['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc', 'mel']
+    return class_names[predicted_class]
 
-# Preprocess input image and make predictions
-image = preprocess_image("path_to_image.jpg")  # Custom image preprocessing function
-prediction = model.predict(image)
+# Example usage
+image_path = 'your_image.jpg'
+prediction = predict(image_path)
+print(f'The predicted class is: {prediction}')