Fine-tuning deep learning models on microscopic images of liver and intestine cells of shrimps using k-fold cross-validation

Abstract

We employ the fine-tuning technique to train compact and efficient deep convolutional neural networks—specifically MobileNet_V2, MobileNet_V3_Small, and MobileNet_V3_Large – to classify the nutritional status of farmed shrimp. The classification is based on microscopic images of liver and intestinal cells, enabling rapid and scalable assessment of shrimp health through image-based diagnostics. The experiment was conducted on a dataset comprising 854 cellular images, and used k-fold cross-validation to split the dataset into the training and test sets. The pre-trained MobileNet_V3_Large was fine-tuned on our cellular image dataset using 10-fold cross-validation, achieving the highest average classification accuracy of 90.89%. This study demonstrates the potential of applying deep learning techniques to the monitoring and nutritional management of farmed shrimp, aiming to enhance productivity in aquaculture operations.