Merge pull request #1669 from recodehive/sanjay-kv-patch-2

Update ReadMe.md

Author: sanjay-kv

Detection Models/Blood Cell Cancer Detection using CNN and EfficientNetB3/ReadMe.md

@@ -1,11 +1,15 @@
+
 # Blood Cell Cancer Classification using CNN and EfficientNetB3
 
-This project aims to classify blood cell images to detect cancerous cells using Convolutional Neural Networks (CNN) and EfficientNetB3 architecture.
+![License](https://img.shields.io/badge/license-MIT-blue.svg)
+![Status](https://img.shields.io/badge/status-active-success.svg)
+![Contributions](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)
 
 ## Table of Contents
 - [Overview](#overview)
 - [Dataset](#dataset)
 - [Installation](#installation)
+- [Getting Started](#getting-started)
 - [Notebook Structure](#notebook-structure)
   - [Import Necessary Libraries](#import-necessary-libraries)
   - [Reading the Data](#reading-the-data)
@@ -17,15 +21,18 @@ This project aims to classify blood cell images to detect cancerous cells using
   - [Using EfficientNetB3](#using-efficientnetb3)
   - [Conclusion](#conclusion)
 - [Results](#results)
+- [Future Work](#future-work)
+- [Authors](#authors)
+- [License](#license)
 
 ## Overview
-This project utilizes deep learning techniques to classify blood cell images into cancerous and non-cancerous categories. Initially, a basic CNN model is implemented, followed by an enhanced model using EfficientNetB3 architecture for improved accuracy.
+This project aims to classify blood cell images to detect cancerous cells using deep learning techniques, specifically Convolutional Neural Networks (CNN) and EfficientNetB3 architecture. The goal is to develop a robust model that can accurately differentiate between cancerous and non-cancerous blood cells.
 
 ## Dataset
-The dataset used in this project is sourced from [Kaggle](https://www.kaggle.com/) and contains images of various blood cell types. The dataset is organized into folders for each cell type.
+The dataset used in this project is sourced from [Kaggle](https://www.kaggle.com/), containing images of various blood cell types. The dataset is organized into folders for each cell type.
 
 ## Installation
-To run the notebook, ensure you have the following libraries installed:
+Ensure you have the following libraries installed:
 - TensorFlow
 - Keras
 - NumPy
@@ -35,11 +42,22 @@ To run the notebook, ensure you have the following libraries installed:
 - OpenCV
 - PIL
 
-You can install the required libraries using:
+Install the required libraries using:
 ```bash
 pip install tensorflow keras numpy pandas matplotlib seaborn opencv-python pillow
 ```
 
+## Getting Started
+1. Clone the repository:
+    ```bash
+    git clone https://github.com/recodehive/machine-learning-repos.git
+    ```
+2. Navigate to the project directory:
+    ```bash
+    cd machine-learning-repos/Detection Models/Blood Cell Cancer Detection using CNN and EfficientNetB3
+    ```
+3. Ensure you have the required libraries installed as mentioned in the [Installation](#installation) section.
+
 ## Notebook Structure
 
 ### Import Necessary Libraries
@@ -70,4 +88,19 @@ EfficientNetB3 architecture is used to enhance the model's accuracy. The pre-tra
 Summary of the findings and results, including insights on model performance and potential improvements.
 
 ## Results
-The project demonstrates the capability of CNN and EfficientNetB3 in classifying blood cell images with high accuracy.
+The project demonstrates the capability of CNN and EfficientNetB3 in classifying blood cell images with high accuracy. The final model achieved an accuracy of XX% on the validation set.
+
+## Future Work
+- Explore the use of other pre-trained models.
+- Implement more advanced data augmentation techniques.
+- Deploy the model as a web application for real-time predictions.
+
+## Authors
+- [Sanjay KV](https://github.com/sanjay-kv)
+
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+---
+
+You can copy and paste this improved version into your ReadMe.md file.