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Predict which cell is cancerous with 96% accuracy using SVM machine learning algorithm.
This application aims to early detection of lung cancer to give patients the best chance at recovery and survival using CNN Model.
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%
This is a repo for the Tanzania AI lab hackathon 2020 & the AI4Dev2020 challenge, where we as the Elixir team created the 1st AI based cancer diagnosis system, built a model comprising of Deep Convolutional Neural Network(CNN) and a web app that screens microscopic images so as to detect cancer tumors, thus increasing speed, accuracy in cancer diagnosis, and testing
DeepHealth Annotate is a web-based tool for viewing and annotating DICOM images. Annotation metadata can be exported in JSON format to be used for a variety of purposes, such as creating training input for deep learning models that use bounding box algorithms.
Team Capybara final project "Histopathologic Cancer Detection" for the Statistical Machine Learning course @ University of Trieste
A PyTorch implementation of MedSegDiff, a diffusion probabilistic model designed for medical image segmentation.
Submission for Google Gen AI Hackathon
Segmentation of skin cancers
Análisis de se?ales fotoacústicas de cáncer en mamas ex vivo por medio de la transformada de Wavelet
Breast Cancer Diagnosis