You seem to be looking for probabilistic classification where in addition to outputting the most likely class the input belongs to , you are also looking for the probabilities of the input belonging to each of the output classes. There are a variety of models that can be fit to your data using the Classification Learner App. Some of these models do support probabilistic classifiers , a good example would be the SVM or support vector machine model. I will briefly describe below how to obtain the class probabilities for this model below.
Using the data and various input features, you can train the SVM model in the Classification Learner App and then export the model once you are satisfied with the training results. Alternatively you may use the fitcsvm function to achieve the same. Now, with your model you may use the predict method (documentation here :https://www.mathworks.com/help/stats/compactclassificationsvm.predict.html ) to find the posterior probability (which is just the conditional probability that your input belongs to a certain class given the training data) that an observation belongs to a certain class. You can take a look at the examples in the documentation link above to help you get started.
One of the limitations of the SVM model is that it works well only for binary classification problems. From your question I gather that this is the case as you seem to be looking to classify a tissue into one of two classes ('this' or 'that' presumably 'cancerous' or 'not cancerous'). In case this is not what you are looking for you may have to look at other models to obtain probabilistic classification (say logistic regression).
Finally a quick note about PRTools: This is a third party toolbox so while we encourage you to try it out, it may be a good idea to contact the authors directly to get a better understanding of the functionality it provides.