Department of Instrumentation and Control Engineering, NSS College of Engineering Palakkad, Kerala, India (Affiliated to APJ Abdul Kalam Technological University, Kerala, India.
Department of Gynaecology, College of Veterinary and Animal Sciences, Mannuthy, Kerala, India.
BMC Vet Res. 2024 Sep 9;20(1):401. doi: 10.1186/s12917-024-04242-1.
Successful identification of estrum or other stages in a cycling bitch often requires a combination of methods, including assessment of its behavior, exfoliative vaginal cytology, vaginoscopy, and hormonal assays. Vaginoscopy is a handy and inexpensive tool for the assessment of the breeding period. The present study introduces an innovative method for identifying the stages in the estrous cycle of female canines. With a dataset of 210 vaginoscopic images covering four reproductive stages, this approach extracts deep features using the inception v3 and Residual Networks (ResNet) 152 models. Binary gray wolf optimization (BGWO) is applied for feature optimization, and classification is performed with the extreme gradient boosting (XGBoost) algorithm. Both models are compared with the support vector machine (SVM) with the Gaussian and linear kernel, k-nearest neighbor (KNN), and convolutional neural network (CNN), based on performance metrics such as accuracy, specificity, F1 score, sensitivity, precision, matthew correlation coefficient (MCC), and runtime. The outcomes demonstrate the superiority of the deep model of ResNet 152 with XGBoost classifier, achieving an average model accuracy of 90.37%. The method gave a specific accuracy of 90.91%, 96.38%, 88.37%, and 88.24% in predicting the proestrus, estrus, diestrus, and anestrus stages, respectively. When performing deep feature analysis using inception v3 with the same classifiers, the model achieved an accuracy of 89.41%, which is comparable to the results obtained with the ResNet model. The proposed model offers a reliable system for identifying the optimal mating period, providing breeders and veterinarians with an efficient tool to enhance the success of their breeding programs.
成功识别发情或其他发情周期阶段的母犬通常需要结合多种方法，包括评估其行为、阴道脱落细胞学检查、阴道镜检查和激素检测。阴道镜检查是评估繁殖期的一种方便且廉价的工具。本研究提出了一种识别雌性犬发情周期阶段的创新方法。该方法使用包含四个繁殖阶段的 210 张阴道镜图像数据集，通过 inception v3 和 Residual Networks (ResNet) 152 模型提取深度特征。使用二进制灰狼优化（Binary Gray Wolf Optimization，BGWO）算法进行特征优化，并使用极端梯度提升（Extreme Gradient Boosting，XGBoost）算法进行分类。根据准确性、特异性、F1 评分、灵敏度、精度、马修相关系数（Matthew Correlation Coefficient，MCC）和运行时间等性能指标，将这两种模型与支持向量机（Support Vector Machine，SVM）的高斯核和线性核、k-最近邻（k-Nearest Neighbor，KNN）和卷积神经网络（Convolutional Neural Network，CNN）进行比较。结果表明，ResNet 152 与 XGBoost 分类器的深度模型具有优越性，平均模型准确性为 90.37%。该方法在预测发情前期、发情期、发情后期和静止期时的特定准确性分别为 90.91%、96.38%、88.37%和 88.24%。当使用具有相同分类器的 inception v3 进行深度特征分析时，模型的准确性为 89.41%，与 ResNet 模型的结果相当。该模型为识别最佳交配期提供了一个可靠的系统，为繁殖者和兽医提供了一个提高繁殖计划成功率的有效工具。
