Deep learning approach for classifying contamination levels with limited samples

Abstract

The detection of the contaminants in daily food and drinking water is crucial for global public health. For heavy metals detection of Mercury (Hg) and Arsenic (As), our group has proposed a novel paper-based and microfluidic device integrated with a mobile phone and an image analysis pipeline to capture and analyze the sensor images on-site. Still, the detection of lower contamination levels remains challenging due to the small number of available data samples and large intra-class variance of our application. To overcome this challenge, we explore traditional data augmentation and GAN-based augmentation techniques for synthesizing realistic colorimetric images; and we propose a CNN classifier for five-contamination-levels classification. Our proposed system is trained and evaluated on a limited dataset of 126 phone captured images of five contamination levels. Our system yields 88.1% classification accuracy and 91.92% precision, demonstrating the feasibility of this approach. We believe that this approach of training deep learning models on limited detection images datasets presents a clear path toward phone-based contamination-levels detection.