Deep Learning for Malaria Detection
Use case in Healthcare Intelligence
Machine Learning and Deep Learning models combined with easy to build open source techniques can help improve the diagnosis of the life-threatening Malaria disease.
The objective of this usecase is to develop a model that can predict the probability of a human cell to be infected with Malaria parasite from an exploratory data analysis performed on a vast dataset containing images of infected and uninfected human cells. We leveraged deep learning models like CNN because of its effectiveness in providing solutions related to Computer Vision tasks. Using a CNN model we have been successful to predict both the categories and validate our approach to the future unseen data.
To know how Qualetics gives an effective solution, download the full usecase.
About Qualetics Data Machines
Data Analytics and Artificial Intelligence are growing to be a ubiquitous need in the modern enterprise ecosystem. The need for Analytics is ranging from basic Descriptive and Diagnostic Analytics to advanced Predictive, Prescriptive, and Cognitive Analytics. However, the barrier of entry is high due to expensive infrastructure and highly skilled resource requirements.
Qualetics Data Machines Inc. aims to eliminate this barrier by introducing a product that makes it easy for businesses to embrace AI and gain data intelligence.