Azam Bakht, Ur Rahman Sami, Irfan Muhammad, Awais Muhammad, Alshehri Osama Mohammed, Saif Ahmed, Nahari Mohammed Hassan, Mahnashi Mater H
Department of Computer Science and IT, University of Malakand, Chakdara 18801, Pakistan.
College of Engineering, Electrical Engineering Department, Najran University, Najran 61441, Saudi Arabia.
Entropy (Basel). 2020 Sep 17;22(9):1040. doi: 10.3390/e22091040.
Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood-microcytic hyperchromic anemia-which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0∘, 45∘, 90∘, and 135∘). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases.
对各种血细胞样本进行准确的血涂片定量分析具有重要的临床意义。传统的血涂片定量手工操作过程非常耗时且容易出错。因此，本文提出了对人类血液中最常见病症——小红细胞高色素性贫血（这是多种危及生命疾病的病因）的自动检测方法。此任务首先通过对血液成分（即红细胞、白细胞和血小板）进行分割来完成。然后，从每个红细胞中提取最具影响力的特征，如几何形状描述符、灰度共生矩阵（GLCM）、灰度游程长度矩阵（GLRLM）以及Gabor特征（均方能量和平均幅度）。为了在其他红细胞类别中区分出低色素小红细胞，以（0°、45°、90°和135°）角度进行扫描。为了实现高精度，使用用于不平衡学习的自适应合成（AdaSyn）采样来平衡数据集，并使用局部敏感判别分析（LSDA）技术进行特征约简。最后，利用这些特征，使用多层感知模型和随机森林学习算法对血细胞进行分类。准确率达到了96%，优于现有技术的性能。这项工作的最终成果可能有助于制定一种具有成本效益的筛查方案，从而在全球范围内实现低成本的血涂片分析筛查，进而实现这些疾病的早期检测。
