Shah Syed Sajid Hussain, Elmorsy Ekramy, Othman Rashad Qasem Ali, Syed Asmara, Armaghan Syed Umar, Khalid Bokhari Syed Usama, Elmorsy Mahmoud E, Bawadekji Abdulhakim
Department of Pathology, Northern Border University, Arar, SAU.
Department of Pathology and Laboratory Medicine, Northern Border University, Arar, SAU.
Cureus. 2024 Apr 17;16(4):e58496. doi: 10.7759/cureus.58496. eCollection 2024 Apr.
The current study used the deep machine learning approach to differentiate human blood specimens from cow, goat, and chicken blood stains based on cell morphology.
A total of 1,955 known Giemsa-stained digitized images were acquired from the blood of humans, cows, goats, and chickens. To train the deep learning models, the well-known VGG16, Resnet18, and Resnet34 algorithms were used. Based on the image analysis, confusion matrices were generated.
Findings showed that the F1 score for the chicken, cow, goat, and human classes were all equal to 1.0 for each of the three algorithms. The Matthews correlation coefficient (MCC) was 1 for chickens, cows, and humans in all three algorithms, while the MCC score was 0.989 for goats by ResNet18, and it was 0.994 for both ResNet34 and VGG16 algorithms. The three algorithms showed 100% sensitivity, specificity, and positive and negative predictive values for the human, cow, and chicken cells. For the goat cells, the data showed 100% sensitivity and negative predictive values with specificity and positive predictive values ranging from 98.5% to 99.6%.
These data showed the importance of deep learning as a potential tool for the differentiation of the species of origin of fresh crime scene blood stains.
本研究采用深度机器学习方法，基于细胞形态将人类血液样本与牛、山羊和鸡的血迹区分开来。
从人类、牛、山羊和鸡的血液中获取了总共1955张已知的吉姆萨染色数字化图像。为了训练深度学习模型，使用了著名的VGG16、Resnet18和Resnet34算法。基于图像分析，生成了混淆矩阵。
结果表明，对于三种算法中的每一种，鸡、牛、山羊和人类类别的F1分数均等于1.0。在所有三种算法中，鸡、牛和人类的马修斯相关系数（MCC）均为1，而ResNet18算法对山羊的MCC分数为0.989，ResNet34和VGG16算法对山羊的MCC分数均为0.994。这三种算法对人类、牛和鸡的细胞显示出100%的敏感性、特异性以及阳性和阴性预测值。对于山羊细胞，数据显示敏感性和阴性预测值为100%，特异性和阳性预测值在98.5%至99.6%之间。
这些数据表明深度学习作为区分新鲜犯罪现场血迹来源物种的潜在工具的重要性。
