Malaria Cell Image Classification using Autoencoder

Over the years, many researchers have sought to use Deep Learning techniques to detect the malaria-infected cells in blood sample images. Although extremely dangerous, the spread of malaria can be restricted when treated in the early stages. This serves as an impetus for implementing an accurate solution for the detection of malaria that can replace the traditional manual process. The manual process consists of visually examining the blood samples and counting the parasitized and non-parasitized red blood cells. This process is extremely time-consuming, requires the presence of trained medical personnel, and is susceptible to human errors. With these aspects in mind, we aimed to develop a solution that could be used by medical staff with minimal training, thereby saving on time and labour. Having studied various research papers related to the use of Deep Learning techniques for the detection of malaria, we have proposed a model that addresses the gaps we identified in these systems while not compromising on the accuracy of the results. Our proposed model comprises a pre-processing module, the frozen Encoder of an Autoencoder model, a few dense CNN layers, and the classifier (Softmax).