Identification of uncontrolled accumulation of abnormal blood cells ( lymphoblasts ) considered to be a challenging task. Despite a wide variety of image processing and deep learning techniques, the task of extracting the features from Acute Lymphoblastic Leukemia (ALL) images and detection of ALL cells is still challenging and complex issue due to morphological variations in cells. In order to overcome these drawbacks, in this study, we proposed a new framework with a combination of spiking and residual network for the detection and classification of lymphoblasts cells from healthy ones in blood sample images. According to this, features are extracted using a novel First-Spike-based approach, and then the Gaussian function is applied to remove the low-intensity edges. To reduce dimensionality, Principal Component Analysis ( PCA ) is used and finally, a developed deep residual architecture is employed to diagnose the ALL blood cells from the reconstructed images. To show the effectiveness of the proposed model, it is evaluated on microscopic images of blood samples from ALL Images (ALL- IDB ) and ISBI -2019 C- NMC dataset. The results show the superiority of the model to be an appropriate choice for future biomedical imaging tasks.