Detecting malaria with deep learning - Malaria is a deadly, infectious, mosquito-borne disease caused by Plasmodium parasites that are transmitted by the bites of infected female Anopheles mosquitoes. There are five parasites that cause malaria, but two types—P. falciparum and P. vivax—cause the majority of the cases.
Some advancements have been made in leveraging state-of-the-art image processing and analysis techniques to extract hand-engineered features and build machine learning-based classification models. However, these models are not scalable with more data being available for training and given the fact that hand-engineered features take a lot of time.
Deep learning models, or more specifically convolutional neural networks (CNNs), have proven very effective in a wide variety of computer vision tasks.
Convolution layers learn spatial hierarchical patterns from data, which are also translation-invariant, so they are able to learn different aspects of images. For example, the first convolution layer will learn small and local patterns, such as edges and corners, a second convolution layer will learn larger patterns based on the features from the first layers, and so on. This allows CNNs to automate feature engineering and learn effective features that generalize well on new data points. Pooling layers helps with downsampling and dimension reduction. CNNs help with automated and scalable feature engineering. Also, plugging in dense layers at the end of the model enables us to perform tasks like image classification. Automated malaria detection using deep learning models like CNNs could be very effective, cheap, and scalable, especially with the advent of transfer learning and pre-trained models that work quite well, even with constraints like less data.
The data for our analysis comes from researchers at the Lister Hill National Center for Biomedical Communications (LHNCBC): https://lhncbc.nlm.nih.gov/LHC-downloads/downloads.html#project-a232
I have done CNN from scratch and have also used deep trandfer learning (using VGG 19).