MorphoNet: A Novel Bivalve Images Classification Framework with Convolutional Neural Network

Abstract

. The bivalves' morphometric analysis of the freshwater shell characteristics is based on the shell size, shape, tooth, scars, and texture . We experimented and compared the accuracies of the following popular convolutional neural network architectures : ResNeSt, MobileNet, VGG16, Transfer Learning, and EfficientNet, whose model trainings are based on the bivalve image dataset obtained from a biology laboratory . The MobileNet model that gives the highest accuracy rate by 72 % is selected to be a classification model of our framework named MorphoNet . We also applied the YOLO4 object detection in the MorphoNet to detect the teeth and scars on the bivalve image . The framework can identify the bivalve class labels and detect the interesting features on the bivalve images automatically . It is an alternative tool to help the biologists in a preliminary class label identification and support the land - marking creation and morphometric analysis instead of doing it by hand .