Skip to content

Latest commit

 

History

History
79 lines (60 loc) · 3.16 KB

README.md

File metadata and controls

79 lines (60 loc) · 3.16 KB
Raw

Cancer Detection using Neural Networks

This repository contains code for detecting cancer using a simple neural network built with TensorFlow and Keras. The dataset used in this project includes various features extracted from cell nuclei images. The goal is to predict whether a tumor is malignant (cancerous) or benign based on these features.

Table of Contents

Overview

Cancer detection is one of the critical applications in healthcare. This project demonstrates how to train a neural network to classify tumors as malignant (1) or benign (0) based on specific cell measurements. The dataset is split into training and testing sets to evaluate the model's performance.

The neural network consists of three dense layers and uses the sigmoid activation function to output a probability indicating the likelihood of the tumor being malignant.

Dataset

The dataset used for this project is expected to be in CSV format (CancerStats.csv). The dataset should contain various features for each tumor, and the target variable is the diagnosis(1=m, 0=b) column, where 1 represents malignant and 0 represents benign tumors.

Columns in the dataset:

  • The features are various characteristics of the tumor, such as size, texture, and smoothness.
  • The target variable is the diagnosis(1=m, 0=b) column, where:
    • 1 stands for malignant (cancerous)
    • 0 stands for benign

Model Architecture

The neural network model consists of three layers:

  1. Input Layer: A dense layer with 256 units, using a sigmoid activation function.
  2. Hidden Layer: A dense layer with 256 units, using a sigmoid activation function.
  3. Output Layer: A dense layer with 1 unit and sigmoid activation for binary classification.

Model Compilation:

  • Optimizer: Adam
  • Loss function: binary_crossentropy
  • Metric: accuracy

Model Summary:

Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 256)               <calculated_param_count>
_________________________________________________________________
dense_1 (Dense)              (None, 256)               <calculated_param_count>
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 <calculated_param_count>
=================================================================

Installation

To get started with this project, follow these steps:

Clone the repository:

git clone https://github.com/kiruthikpurpose/CancerDetectionModel.git

Install the necessary Python dependencies:

pip install -r Requirements.txt

Ensure that the dataset (CancerStats.csv) is in the root folder of the project.

Usage

To train the model, run the following command:

python CancerDetectionModel.py

This script will:

Load the dataset from CancerStats.csv Split it into training and testing sets Define the neural network model Train the model for 100 epochs Evaluate the model on the test set