I have two annual measurements taken on medical images depicting a lung cancer tumor 's condition. I have likelihood function that taken in the measurement values and estimates malignancy of the tumor. I want to consider cancer or no cancer as the two states of the tumor and the state of the tumor in the first dataset is also known.
Can a particle filter be designed to diagnose the cancer in tumor using the second measurements given the state and measurement in the last/first annual measurements? I also have figured out that an exponential distribution can be used as a state transition model. I have interpolated few data points between the two annual measurements just in case if Particle filter requires some more data for training. But i am unable to put all this information together to make the tumor diagnosis if this is a good case for filtering ?