Binary-Class Image Classification Deep Learning Model for Malaria Parasite Detection Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Malaria Parasite Detection dataset is a binary-class classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Biomedical researchers have developed a mobile application that runs on a standard Android smartphone attached to a conventional light microscope for detecting malaria disease. The smartphone’s built-in camera acquired thin blood smear images of slides for each microscopic field of view. An expert manually annotated the slides afterward. The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells.

In this Take1 iteration, we will construct a CNN model using a simple three-block VGG architecture and test the model’s performance using a held-out validation dataset.

ANALYSIS: In this Take1 iteration, the model’s performance achieved an average accuracy score of 94.08% on the validation dataset after 20 epochs.

CONCLUSION: In this iteration, the simple three-block VGG CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Malaria Parasite Detection

Dataset ML Model: Binary-class image classification with numerical attributes

Dataset Reference:

A potential source of performance benchmark: or

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.