Using a sssNet Convolutional Neural Network (CNN) with Support Vector Machine (SVM) algorithm to identify formalin presence in images of eyes of Chanos chanos (milkfish)
THOREENZ P. SOLDEVILLA, DOMINIC NATHANIEL ANTHONY A. REDANIEL, LANCE CHRISTIAN V. SY, and MARIA MILAGROSA A. NULLA
Philippine Science High School Western Visayas Campus – Department of Science and Technology (DOST-PSHSWVC), Brgy. Bito-on, Jaro, Iloilo City 5000, Philippines
Using image processing, eye turbidity of formalin-treated Chanos chanos (milkfish) was statistically proven to be significantly different from those untreated. However, automation of such processes was yet to be explored. This study aims to use a sssNet Convolutional Neural Network (CNN) with Support Vector Machine (SVM) algorithm to identify formalin presence in milkfish. Ninety percent of 420 formalin-treated milkfish images and 420 untreated images, each with an indicated day of image capture, were subjected to feature extraction and classification using sssNet-SVM. The remaining 10% of the dataset was used to validate the algorithm’s performance. The algorithm garnered 98.16 to 99.15% validation accuracy for identifying formalin presence. However, seven-day feature map analysis reveals that the algorithm struggles to determine formalin presence in treated samples using their images that were captured one or two days after the samples’ dousing in formalin.
Keywords: sssNet, Chanos chanos, Neural Network, automation, formalin