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Human‐centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision‐making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer‐aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical‐imaging‐related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning‐based human‐centric biomedical diagnosis model for acute lymphoblastic detection …