🩺 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.

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

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

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

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.

Over the past few decades, ML techniques have been widely used in intelligent healthcare systems, especially for breast cancer (BC) diagnosis and prognosis. Traditionally the diagnostic accuracy of a patient depends on a physician’s experience. however, this expertise is built up over many years of observations of different patient’s symptoms and confirmed diagnoses. ML techniques can take over some complex manual works from the physicians. Recently, ML techniques are playing a significant role in diagnosis of BC by applying classification techniques to identify people with BC, distinguish benign from malignant tumours and to predict weather the patient is affected or not. We focus on the neural network (NN), support vector machine (SVMs) and k-nearest neighbor (k-NNs) techniques in BC diagnosis.

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This project demonstrates how to use neural networks for binary classification problems like breast cancer diagnosis. The model successfully predicts whether a tumor is malignant or benign using the Breast Cancer Wisconsin dataset. The neural network model achieves high accuracy, and the performance is tracked and visualized using various plots.

Breast Cancer Classification using Neural Network in Python. This is the first Deep Learning Project in our channel. Here we build a simple Neural Network (NN) with Tensorflow and Keras in Python.

This repository contains the code and documentation for the "Breast Cancer Classification with Neural Network" project. The primary goal of this project is to develop a robust and accurate deep learning model to classify breast cancer as either malignant or benign using medical imaging data.

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Breast cancer is a leading cause of death among women worldwide. Manual diagnosis is time-consuming and error-prone. There is a need for an automated system for accurate classification of tumors as benign or malignant.

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Breast Cancer Classification with Neural Network | Deep Learning Project

This repository contains a Deep Learning project - Breast Cancer Classification with Neural Network.

Web application : Breast cancer classification and prediction with multi-view deep convolutional neural networks

Breast cancer classification and prediction with multi-view deep convolutional neural networks using machine learning

Breast Cancer Classification Model classifies breast cancer images as benign or malignant using a Convolutional Neural Network (CNN) built with TensorFlow and Keras.

Binary classification of breast cancer tumors using a regularized Artificial Neural Network (ANN) built with TensorFlow.

Breast cancer binary classification using the sklearn data set along with logistic regression with a Neural Network mindset

This project demonstrates breast cancer classification using a Multi-Layer Perceptron (MLP) neural network implemented with scikit-learn.

Binary classification of breast cancer tumors using a neural-network–based model with standardized features and TensorFlow/Keras.

A deep learning framework using VGG-16-based Convolutional Neural Networks for breast cancer detection and classification from mammographic images with additional model comparisons.

🩺 Breast Cancer Classification using PyTorch | 🤖 Neural Networks for Benign vs Malignant Prediction | 📈 96% Accuracy ⚡ Deep Learning with PyTorch | 🧠 Breast Cancer Tumor Detection | ✅ High Accuracy on Real-World Data 📊 Binary Classification of Breast Cancer | 🔬 Healthcare AI Project in PyTorch | 🚀 GPU-Ready 🤖 PyTorch Deep Learning Model

Developed a robust image classification and segmentation system for breast cancer diagnosis using Convolutional Neural Networks (CNNs). Implemented transfer learning with MobileNet architecture on a comprehensive breast cancer image dataset. Leveraged OpenCV for image preprocessing and analysis, enhancing model accuracy.