A total of 244 patients were analyzed, 99 in Training, 83 in Testing-1 and 62 in Testing-2. Patients were classified into 3 molecular subtypes: TN, HER2+ and (HR+/HER2-). Deep learning using CNN and Convolutional Long Short Term Memory (CLSTM) were implemented. The mean accuracy in Training dataset evaluated using 10-fold cross-validation was higher using CLSTM (0.91) than CNN (0.79). When the developed model was applied to testing datasets, the accuracy was very low, 0.4-0.5. When transfer learning was applied to re-tune the model using one testing dataset, it could greatly improve accuracy in the other dataset from 0.4-0.5 to 0.8-0.9.