Python feed-forward neural network to predict breast cancer. Trained using stochastic gradient descent in combination with backpropagation.

Supervised Learning Algorithms

Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.

Official code for Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

An experiment using neural networks to predict obesity-related breast cancer over a small dataset of blood samples.

The aim of the project, to determine whether the breast cancer cell is malignant or benign.I got the dataset from Kaggle.

Using the Knn algorithm, it detects whether the tumor is benign or malignant in people diagnosed with breast cancer.

Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.

Machine learning classifier for cancer tissues 🔬

Breast Cancer Prediction using Deep Learning and Keras

Comparison of Different Machine Learning Classification Algorithms for Breast Cancer Prediction

A text-based computational framework for patient -specific modeling for classification of cancers. iScience (2022).

Breast cancer prediction ANN model developed with Python.

This CNN is capable of diagnosing breast cancer from an eosin stained image. This model was trained using 400 images. It has an accuracy of 80%

This repository contains code for building a model that can detect breast cancer based on various features of the tumor cells. The model uses logistic regression with data scaling to obtain an accuracy of 98.24% on the test set.

Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides, BCNB Dataset

On-spot training to enhance the performance of traditional machine learning algorithms, applied to the prediction of breast cancer malignity from ultrasound images