The advancement of digital microscopic scanning has made the study of image processing as well as categorization an exciting field of diagnostic studies. The literature describes a number of methods for detecting acute lymphocytic leukemia (ALL) using blood smear pictures. The goal of this research is to create an efficient approach for segmenting and detecting leukemia. This research has created a leukemia diagnosis module predicated on deep learning (DL) using blood smear pictures. Pre-processing, segmentation, extraction of features, and classification are performed here by the identification scheme. The presented hybrid model of African Buffalo and African Vulture Optimization (AB-AVO) performs the segmentation process, in which cytoplasm and nucleus regions are segmented. The Local Directional Pattern (LDP) and color histogram characteristics have been then retrieved from the segmented pictures and given into the presented Recurrent Neural Network (RNN) for categorization. The ALL-IDB1 and ALL-IDB2 databases’ blood smear pictures are taken into account for the investigation and assessed using metrics including F1-score, sensitivity, dice coefficient, precision, specificity, recall, and accuracy. The presented AB-AVO-RNN approach exhibits 100% accuracy, according to simulation data. Modern methodologies are used to compare the effectiveness of the suggested AB-AVO-RNN methodology. The investigation demonstrates that the suggested classifier performs comparably better and can identify leukemia from blood smear pictures.

数字显微扫描的进步使图像处理和分类研究成为令人兴奋的诊断研究领域。文献描述了多种利用血涂片图片检测急性淋巴细胞白血病 (ALL) 的方法。这项研究的目标是创建一种有效的方法来分割和检测白血病。这项研究利用血涂片图片创建了一个基于深度学习（DL）的白血病诊断模块。这里通过识别方案执行预处理、分割、特征提取和分类。提出的非洲水牛和非洲秃鹫优化（AB-AVO）混合模型执行分割过程，其中细胞质和细胞核区域被分割。然后从分割的图片中检索局部方向模式（LDP）和颜色直方图特征，并将其输入到所提出的循环神经网络（RNN）中进行分类。调查中考虑了 ALL-IDB1 和 ALL-IDB2 数据库的血涂片图片，并使用 F1 分数、灵敏度、骰子系数、精确度、特异性、召回率和准确性等指标进行评估。根据仿真数据，所提出的 AB-AVO-RNN 方法具有 100% 的准确度。现代方法用于比较建议的 AB-AVO-RNN 方法的有效性。调查表明，所建议的分类器性能相对更好，并且可以从血涂片图片中识别白血病。