Optimizing CNN Hyperparameters for Blastocyst Quality Assessment in Small Datasets

Morphological assessment of blastocyst quality is one of the most significant challenges in the IVF process because the current assessment is based on evaluation by an embryologist; thus, it is still manual and subjective and lacks precision. Artificial intelligence (AI) plays a role in overcoming the limitations of the manual assessment system, and its use is expected to increase implantation rates in IVF. This study aims to optimize the convolutional neural network (CNN) model using the grid search method and to evaluate the effectiveness of different machine learning models in classifying the blastocyst quality in a small dataset. The reliability of the proposed model will be compared with that of other machine learning methods, such as logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), the boosting algorithm, and with the addition of the Canny operator as a segmentation process and principal component analysis (PCA) as a feature extraction approach. We evaluated the results using various performance measures, such as the precision, recall, F1-measure, accuracy, and area under the curve of the receiver operating characteristic curve (AUC-ROC). The final results showed that our proposed CNN model achieves a validation accuracy of 84.00%, a test accuracy of 83.33%, and an AUC of 0.844. McNemar’s statistical test results support that our CNN model outperforms the other classifiers.