BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

Master's dissertation for breast cancer detection in mammograms using deep learning techniques in Tensorflow. Contains the final report and source code.

Official Pytorch implementation of MICCAI 2024 paper (early accept, top 11%) Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography

Code for Paper: Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

This project is made in Matlab Platform and it detects whether a person has cancer or not by taking into account his/her mammogram.

This project uses mammograms for breast cancer detection using deep learning techniques.

Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN

SVM (Support Vector Machines) is used to build and train a model using human cell records, and classify cells to predict whether the samples are benign or malignant and display output using Flask Application On Heroku

Detect Breast Cancer using ANN and Random Forest

Code for published article in PLOS ONE. Breast cancer detection in mammograms using DL techniques. Contains source code for data and methods used.

ONCO is a cancer diagnosis/prognosis mobile application focused on the 3 main cancers of the thoracic region (Breast, Lung & Skin)

DiagnoSys is a comprehensive web application that provides advanced detection and analysis for various health conditions. This project leverages state-of-the-art machine learning algorithms to detect and diagnose COVID-19, Alzheimer's disease, breast cancer, and pneumonia using X-ray and MRI datasets.

An implementation of the Multilayer Perceptron for breast cancer detection using the Wisconsin diagnostic dataset.

Breast Cancer Detection Using Machine Learning Project Code, PPT, Synopsis, Report and Video Explanation

The implementation of our pricai18 paper "Multiple Visual Fields Cascaded Convolutional Neural Network for Breast Cancer Detection."

SVM for Breast Cancer Detection from mammogram Using MatLAB

Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).

Self-Supervised Vision Transformers for Breast Histopathology Image Embeddings in Invasive Ductal Carcinoma Detection

Streamlit application to classify cancer as malignant or benign.