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Current diagnoses of leukemia are typically performed manually by physicians on the basis of blood cell morphology, leading to challenges such as excessive workload, limited efficiency, and subjective outcomes. To solve the above problems, a two-stage detection method was developed for the automatic detection and identification of blood cells. First, for the blood cell detection task, an improved YOLOv7 blood cell detection model was proposed that integrates multihead attention and the SCYLLA-IoU (SIoU) loss function to accurately locate and classify white blood cells (WBCs), red blood cells (RBCs), and platelets in a full-field image of blood cells. For the white blood cell identification task of detecting network positioning, an improved EfficientNetv2 classification model was subsequently developed, which integrates the atrous spatial pyramid pooling (ASPP) module to increase classification accuracy and employs the balanced cross-entropy (BCE) function to address sample number imbalance. The experiments utilized four publicly accessible datasets: BCCD, LDWBC, LISC, and Raabin. The proposed detection model achieved an average accuracy of 94.7% in detecting and identifying blood cells in the BCCD dataset. With an IoU equal to 0.5, the model attained a mean average precision (mAP) of 97.17%. In the white blood cell classification task, an average precision (AP) of 95.12% and an average recall (AR) of 97% were achieved on the LDWBC, LISC, and Raabin datasets. The experimental results demonstrate that the proposed two-stage detection method detects and identifies blood cells accurately, thereby facilitating automatic detection, classification, and quantification of blood cell images, which can aid doctors in preliminary leukemia diagnosis.