🤖 ANN for breast cancer detection (malignant/benign). Includes model training, evaluation, and prediction functions. Built with TensorFlow.

Breast cancer detection using ANN and meta learning

This repository contains the Neural network model to classify Breast Cancer Phase at an accuracy of 92.98%.

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Using ANN for building a Breast Cancer Detection Model

Breast Cancer Detection using CNN-Have done extensive research using CNN, ANN and AI to detect and analyse breast cancer and developed a model for detection.

Detecting Breast Cancer Using an Artificial Neural Network (ANN): This project utilizes a neural network to classify breast cancer cases as malignant or benign. Leveraging the power of deep learning, the model is trained on a dataset of clinical features to provide accurate, efficient cancer detection.

"A Convolutional Neural Network (CNN) project for image classification tasks, featuring trained models for Fashion MNIST dataset and breast cancer detection using ANN. Includes a Flask app for testing and deployment." You can copy and paste this into the description field when creating the repository on GitHub.

Breast cancer is one of the commonest cause of cancer deaths in women. It starts developing when threatening bumps start forming from the breast cells, and unfortunately most diagnoses happen in later stages, thus resulting in low chances of survival for the patient. So for early detection and prognosis, it is necessary to detect the benign or threatening nature of the bumps. In this paper, Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, in order to evaluate the attributes of a breast cancer development at beginning phases and classify it as malignant or benign. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%.

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This project focuses on developing an intelligent system that predicts whether a breast tumor is benign or malignant using Deep Learning techniques. Early detection of breast cancer plays a crucial role in improving treatment outcomes and survival rates, and this project demonstrates how Artificial Intelligence can assist in medical diagnosis.

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Based on the "Breast Cancer Wisconsin (Diagnostic) Data Set", using Artificial Neural Network, we have to predict whether the cancer is Benign or Malignant.

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Breast Cancer Classification on Wisconsin (Original) data using Artificial Neural Network

This project involves applying of the concepts of ANN to detect the breast cancer using various features

This a Breast Cancer Detection using ANN( Artificial Neural Network Model) with 100 % Validation Accuracy and 95 % Testing Accuracy

This repository contains all the files that I used in making the breast cancer detection model.

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Here we apply Feed forward ANN and apply machine learning algorithms like Random Forest, SVM, Linear SVC, nuSVC and finally compute which has higher accuracy and best for detection of breast cance

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In this project i have used boruta as a feature selector to select important features from the dataset and implemented using ANN