What are the possible ways to detect skin while classifying diseases? I am working on a skin disease classification problem where I have successfully created a classifier ( TensorFlow + Keras ) which can classify images of two skin diseases.

The sample image needs to be classified in this manner :


Whether the sample is an image of the skin.
Does the skin have any two of the diseases ( Melanoma or Psoriasis )
If a disease is found, to which class does it belong (CLASS1: Melanoma or CLASS2: Psoriasis



  How can create Classifiers which could carry out the following tasks?
  Do I need the image localization or CNNs or something like YoLo?
  What steps should I implement?


I have created a classifier ( with an accuracy of 96% ) which can classify an image of the two diseases efficiently. But it can't detect the presence of the disease ( Step 2 in the above task list ).
You have to have sample tissues from skins with Melanoma, Psoriasis and Healthy Skins.

As is understood in the question, you developed a solution which can distinguish with 96% accuracy between melanoma and psoriasis. You are missing from the possibility of distinguish between sick and healthy skins.

The model you are looking for is a multiclass classification. Where CLASS3: Healthy skin.

You are doing ok so far, you just need to include this category and images of healthy skins in your model.