End-to-end implementation of Malaria detection using a prior model feature map extractor based on the transfer learning architecture of InceptionResNetV2 plus svm, rfc, xgbost, rslvq and celvq as stand-alone models with options for soft ensemble and hard ensemble based on the prosemble ML package using svm, rslvq and celvq robust prototype-based ML models.
MD ML Webapp is a tool for detecting malaria based on the parasites class and uninfected class. A case study of malaria image cell classification using a combination of pretrained deep-cnn model + traditional ML models with some prototype-based options.
python 3.9 or later with all requirements.txt dependencies installed including xgboost and opencv .
git clone https://github.com/naotoo1/MD.git
cd MD
pip install -r requirements.txtDownload the celvq and svm trained models into the MD folder using the link https://we.tl/t-7UcQgLFuwr
python app.py