end2end-all-conv and Deep-Learning-Approaches-for-Enhanced-Breast-Cancer-Detection

Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 5/25
Maturity 16/25
Community 15/25
Stars: 389
Forks: 137
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
Stars: 12
Forks: 4
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About end2end-all-conv

lishen/end2end-all-conv

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

This project offers deep learning models designed to improve the accuracy of breast cancer detection on screening mammography. It takes standard mammogram images as input and helps identify potential malignant or benign abnormalities, providing insights that can assist radiologists in their diagnostic process. Radiologists and medical researchers focusing on diagnostic imaging would find this tool beneficial.

mammography breast-cancer-screening radiology medical-imaging diagnostic-support

About Deep-Learning-Approaches-for-Enhanced-Breast-Cancer-Detection

harshjuly12/Deep-Learning-Approaches-for-Enhanced-Breast-Cancer-Detection

A deep learning project aimed at early detection of breast cancer by classifying tumors as benign or malignant based on features extracted from cell images. The project demonstrates data preprocessing, model training, and evaluation using various deep learning algorithms to achieve high accuracy in predictions.

This project helps medical professionals identify breast cancer by analyzing features extracted from cell images. It takes raw cell image data and outputs a classification of tumors as either benign or malignant, aiding in early detection. This tool is designed for pathologists, oncologists, and other medical practitioners involved in cancer diagnosis.

cancer-detection medical-diagnosis pathology oncology diagnostic-imaging

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