Update README.md

README.md

@@ -1,4 +1,3 @@
-
 ---
 tags:
 - skin-cancer-detection
@@ -23,27 +22,48 @@ model-index:
         metrics:
           - name: Accuracy
             type: accuracy
-            value: 0.73
+            value: 0.85
 ---
 
 # Skin Cancer Detection Model
 
-This model is trained to detect different types of skin cancer from images using the HAM10000 dataset. The model predicts seven types of skin cancer:
+## 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.
+
+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.
+
+## 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 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.
 
-- **akiec**: Actinic Keratoses and Intraepithelial Carcinoma
-- **bcc**: Basal Cell Carcinoma
-- **bkl**: Benign Keratosis
-- **df**: Dermatofibroma
-- **nv**: Melanocytic Nevus
-- **vasc**: Vascular Lesions
-- **mel**: Melanoma
+## 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:
 
-## Model Information
-The model is a convolutional neural network (CNN) built with TensorFlow and Keras, trained on the HAM10000 dataset.
+| 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. |
 
 ## Usage
-You can use this model in Python by loading it via `keras` or `tensorflow`.
+
+To use this model for inference, you can load it using TensorFlow:
 
 ```python
 from tensorflow.keras.models import load_model
-model = load_model('path_to_model.h5')
+
+# Load the model
+model = load_model("path_to_model.h5")
+
+# Preprocess input image and make predictions
+image = preprocess_image("path_to_image.jpg")  # Custom image preprocessing function
+prediction = model.predict(image)