Identifying Leukemia Subtypes with Deep Learning and Blood Cell Image

Through the evaluation of blood cell pictures, this study uses deep comprehension and interpretive techniques to advance the categorization of leukemia subgroups. A strong convolutional neural network (CNN) structure was created using a deductive method and the idea of interpretivism. Adequate representation was guaranteed via a descriptive approach and secondary gathering of information from various sources. The model demonstrated impressive interpretability as well as accuracy (94.5%) while utilizing layer-wise significance propagation. Evaluation in comparison to conventional diagnostic techniques revealed improved performance. Transparency, bias analysis, and moral considerations were given top priority. Longitudinal research and multimodal interaction should both be investigated in future study. This study has implications for larger classification of hematologic malignancies and advances the field of lymphoma diagnosis.