BCDU-Net : Medical Image Segmentation

Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.

Brain Tumor Detection Using Convolutional Neural Networks.

Breast Cancer Detection Using Machine Learning

Awesome artificial intelligence in cancer diagnostics and oncology

AI-based pathology predicts origins for cancers of unknown primary - Nature

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%

Segmentation of skin cancers on ISIC 2017 challenge dataset.

CNN histopathologic tumor identifier.

simple brain tumor detection using DCNNs

Lung nodule detection- LUNA 16

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.

1st to MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task. (MICCAI 2019) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

Machine learning techniques can be used to overcome these drawbacks which are cause due to the high dimensions of the data. So in this project I am using machine learning algorithms to predict the chances of getting cancer.

Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels

Breast cancer detection using 4 different models i.e. Logistic Regression, KNN, SVM, and Decision Tree Machine Learning models and optimizing them for even a better accuracy.

Early-Stage skin cancer detection using Deep Learning

C++ implementation of oral cancer detection on CT images