<blockquote>
  <p>The intended result for the application: The input is a medical image and the output is the image with <strong>positive regions highlighted</strong> with the overall positive/negative indication with a confidence percentage.</p>
</blockquote>

<p>It sounds like when you say highlighting positive regions, you mean that you want to highlight the region showing the particular disease detected by the classification model. If this is what you mean, I don't believe there is an easy way to combine those two models in the way you described, to the best of my knowledge.</p>

<blockquote>
  <p>The issue with the segmentation app is I need to change the segmentation app from continuous video to single images, and model input characteristics. I don’t know if there is a easy way todo this.</p>
</blockquote>

<p>Changing from real-time video handling to single image handling should be pretty trivial. The second part, model input characteristics, would not be so trivial to change for a number of reasons.</p>

<p>1) The image segmentation (Deeplab) model expects an image as the input. We can't suddenly change the input format and hope it to magically work.</p>

<p>2) The CNN based image classification models are known to be difficult for humans to understand. It simply gives you the label and confidence, but it's based on the entire image. The output doesn't contain any information about what regions in the image contributed the most to the particular (label, confidence) pair. So, if we could somehow modify the segmentation model to take additional information, the image classification model wouldn't give any such useful information in the first place.</p>

<p>Instead, I would suggest searching for some research papers on this topic and see if you can implement those ideas. My quick search gave me <a href="https://arxiv.org/abs/1801.01693" rel="nofollow noreferrer">this paper</a>, for example. And there should be many more papers on medical image classification / segmentation, in which I have no domain knowledge in to help you further.</p>
Tensorflow lite android studio medical diagnostic application using image classification
<p>Problem: I am trying to combine <a href="https://github.com/obeshor/Plant-Diseases-Detector" rel="nofollow noreferrer">https://github.com/obeshor/Plant-Diseases-Detector</a> with <a href="https://github.com/pillarpond/image-segmenter-android" rel="nofollow noreferrer">https://github.com/pillarpond/image-segmenter-android</a> </p>

<p>The first application, Plant app, allows for single images to be processed and the model already works on it.</p>

<p>The second application, image segmentation app, has a great image segmentation features for users and researchers to understand what the model is looking at. </p>

<p>Due to the complexity in the image segmentation application I want to merge the Plant app into the segmentation app.</p>

<p>The issue with the segmentation app is I need to change the segmentation app from continuous video to single images, and model input characteristics. I don’t know if there is a easy way todo this. </p>

<p>The model for the medical image classification is already complete and works on the Plant app.</p>

<p>The intended result for the application:
The input is a medical image and the output is the image with positive regions highlighted with the overall positive/negative indication with a confidence percentage.</p>

<p>Question: Does anyone have any links to methods to combine these two apps or guides to make a new one. I have been working at this for the past five months and am having issues locating the area in the second code for changing model input characteristics, integrating labels, and changing camera from continuous to single images.</p>

<p>Thank you and hope I included enough information. </p>