I’m working on a deep learning task to classify whether a single cell has been exposed to carbon dots or not. Each sample consists of three spatially aligned grayscale microscopy images of the same cell, acquired using different modalities: one brightfield channel and two fluorescence channels highlighting the nucleus and the cell membrane, respectively. Since I’m not an expert in microscopy or biological imaging, I’m unsure whether it is correct to stack all three modalities into a single 3-channel image (as often done with RGB in CNNs). My concern is whether combining brightfield (which is transmitted light) with fluorescence modalities (which are emitted light) into the same tensor might introduce noise, confusion, or inconsistencies for the model. Would an expert in microscopy imaging consider this a flawed approach biologically or visually? Alternatively, would it make more sense to stack only the two fluorescence images (nuclear and membrane), assuming they are more coherent in signal type and structure, and possibly use brightfield separately? It is worth considering whether fluorescence channels, which highlight specific cellular structures, may generally provide more informative features than the brightfield channel for the task of detecting the presence of carbon dots? I’d appreciate any advice from professionals in microscopy, biomedical imaging, or multimodal data analysis on whether this kind of stacking is biologically meaningful and appropriate for classification tasks.