Philadelphia chromosome is a specific genetic abnormality, a reciprocal translocation between chromosome 9 and chromosome 22. This abnormality can cause Chronic Myelogenous Leukemia (CML). Although there are many techniques to diagnose Philadelphia chromosome such as karyotyping, Fluorescence in Situ Hybridizations (FISH), and chromosome painting, etc., these techniques are expensiye and can lead to patients' financial problem. In addition, the number of expert medical technicians who can diagnose chromosomes abnormality is yery low. Thus, it takes many days to inform the result to the patients and increases medical technicians' workloads. This paper proposes a Philadelphia chromosome detection framework using image processing and deep learning techniques. It will help medical technicians to screen the patients who haye the Philadelphia chromosome, so it can reduce the workloads of medical technicians and the time that patients haye to wait for the result. Additionally, this framework can improye the diagnosis of Philadelphia chromosome with less cost.