Machine learning classifier for cancer tissues 🔬
-
Updated
Jun 4, 2021 - Python
Machine learning classifier for cancer tissues 🔬
Official code for Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides, BCNB Dataset
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%
Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.
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.
Pluralistic Image Completion for Anomaly Detection (Med. Image Anal. 2023)
Using the Knn algorithm, it detects whether the tumor is benign or malignant in people diagnosed with breast cancer.
Python feed-forward neural network to predict breast cancer. Trained using stochastic gradient descent in combination with backpropagation.
A text-based computational framework for patient -specific modeling for classification of cancers. iScience (2022).
The aim of the project, to determine whether the breast cancer cell is malignant or benign.I got the dataset from Kaggle.
An experiment using neural networks to predict obesity-related breast cancer over a small dataset of blood samples.
Comparison of Different Machine Learning Classification Algorithms for Breast Cancer Prediction
The repository provides code for running inference with different breast cancer models, links for downloading the trained model checkpoints, and example notebooks on how work with a DICOM pipeline.
Predictive Modelling of Pathological Complete Response Classification and Relapse-Free Survival Regression in Cancer Patients
On-spot training to enhance the performance of traditional machine learning algorithms, applied to the prediction of breast cancer malignity from ultrasound images
This repository contains a comparison of different Naive Bayes classifiers (Bernoulli, Gaussian, and Multinomial) for predicting benign and malignant cancer cases. The project includes confusion matrices for each classifier to evaluate their performance.
Supervised Learning Algorithms
Developed using Python and Google Collab Notebook, this project leverages a Simple Multilayer Perceptron Neural Network (Feed Forward model) for breast cancer prediction. It utilizes the sklearn library for , and model evaluation. The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, Accuracy-95%
Add a description, image, and links to the breast-cancer-prediction topic page so that developers can more easily learn about it.
To associate your repository with the breast-cancer-prediction topic, visit your repo's landing page and select "manage topics."