The application of automated machine learning in Malaria classification

Abstract

As the majority of image classification tasks currently rely on intricate algorithms, crafting specific algorithms for image classification can be a complex and daunting endeavor. In this project, a method for the classification of malaria infected cells using automated machine learning technique is proposed, with the aim of using the Edge Impulse platform to demonstrate that automated machine learning can be used to achieve image classification. More specifically, the project uses the Edge Impulse platform and uses two different modules, Image with Transfer Learning and Image with Classification, and comparing the results of data. The test results were used to analyse the feasibility of the method. In short, the data set is trained and tested using two modules in the Edge Impulse platform, and if both modules achieve a satisfactory accuracy, it demonstrates the feasibility of automated machine learning techniques to image classification. Finally, through the test, the test results demonstrated that the two modules can reach almost 92% of the good accuracy, indicating that automated machine learning technology can be used to replace the algorithms to achieve image classification.