Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)

Multi-Instance-Learning to check breast cancer. An implementation of Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification[arXiv:1504.07947] https://arxiv.org/abs/1504.07947

Breast Cancer classification using deep neural network 🔬

Multilayer Perceptron Neural network for binary classification between two type of breast cancer ("benign" and "malignant" )using Wisconsin Breast Cancer Database

🩺 Advanced neural network for breast cancer classification using Wisconsin dataset. Analyzes cell nucleus characteristics from FNA samples to distinguish malignant/benign masses with 96.5% accuracy. Features comprehensive documentation, automated setup, testing framework, and deployment guides. Educational ML project with 15,000+ lines of docs.

a Breast Cancer Subtype Classification Framework Based on Multi-Omics Attention Neural Networks

Breast Cancer Histology Image Classification Using Deep Neural Networks

Dual-stream hybrid 3D-2D convolution neural network for breast cancer cell classification with polarization images

Hybrid Quantum-Classical Neural Network (QCNN) implementations using MindQuantum for binary classification tasks on Iris and Breast Cancer datasets.

Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data

Interpretable breast cancer classification using convolutional neural networks on mammographic images

This project uses Artiicial Neural Networks (ANN), LSTM, and 1D Convolutional Neural Networks (CNN) to classify breast cancer as malignant or benign using the Breast Cancer Wisconsin dataset.

Course assignment ~ Classification of breast cancer histology images using Convolutional Neural Networks

Machine Learning Breast Cancer Classification involves developing predictive models to classify breast cancer as benign or malignant based on clinical data, such as tumor size and cell features. Using algorithms like logistic regression, SVM, or neural networks, aiding early detection and improving patient outcomes.

This is a project which implements classification on the Wisconsin breast cancer data set, regression on the diamonds data set and clustering on the iris data set. In classification: KNN, Decision Tree, Naive Bayes, Neural Network, Random Forest are the algorithms used. In regression: KNN regression, Decision Tree, Linear Regression, Polynomial Regression, Neural Network and Random Forest are the algorithms used. In clustering, K-Means is the algorithm used. The data is pre-processed prior to being used in any of the above algorithms.

Glandular formation and morphology along with the architectural appearance of glands exhibit significant importance in the detection and prognosis of inflammatory bowel disease and colorectal cancer. The extracted glandular information from segmentation of histopathology images facilitate the pathologists to grade the aggressiveness of tumor. Manual segmentation and classification of glands is often time consuming due to large datasets from a single patient. We are presenting an algorithm that can automate the segmentation as well as classification of H and E (hematoxylin and eosin) stained colorectal cancer histopathology images. In comparison to research being conducted on cancers like prostate and breast, the literature for colorectal cancer segmentation is scarce. Inter as well as intra-gland variability and cellular heterogeneity has made this a strenuous problem. The proposed approach includes intensity-based information, morphological operations along with the Deep Convolutional Neural network (CNN) to evaluate the malignancy of tumor. This method is presented to outpace the traditional algorithms. We used transfer learning technique to train AlexNet for classification. The dataset is taken from MCCAI GlaS challenge which contains total 165 images in which 80 are benign and 85 are malignant. Our algorithm is successful in classification of malignancy with an accuracy of 90.40, Sensitivity 89% and Specificity of 91%. here is a copy of this project from a

Breast cancer classification using Neural Network and Fuzzy Systems

Neural Network for Breast Cancer Classification

Deep Neural Network for Breast Cancer Classification

Feedforward Neural Network Classification for Breast Cancer Wisconsin (Original) Data Set avaliabe in https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)

This project demonstrates binary classification of breast cancer tumors using both logistic regression and a simple neural network built with PyTorch. The dataset used is the Breast Cancer Wisconsin Diagnostic dataset.

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Classification Task for Breast Cancer Medical Patients using Artificial Neural Network

Neural network–based classification model for breast cancer diagnosis using the Breast Cancer Wisconsin dataset, built with TensorFlow and Keras.

System for breast cancer classification using Neural Networks and Particle Swarm Optimizer for IDSS exercise.

subtype classification of breast cancer based on ST-data, using markov random fields and neural networks.

The "Breast Cancer Classification using Neural Networks" project focuses on predicting the presence of breast cancer using deep learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and implementing neural networks.

Develop a graph neural network to integrate proteomic and genomic features for cancer subtype classification (e.g. CPTAC-2 and CPTAC-3 breast, colorectal, and ovarian cancer data)

this project is about a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on on the public INbreast, MIAS and DSM databases.

Machine learning model that predicts malignant vs. benign breast tumors with 97% accuracy using the Wisconsin Breast Cancer Dataset. This project implements multiple classification algorithms (Random Forest, SVM, Neural Networks) with an interactive dashboard for medical professionals to interpret results.