Abstract:
Mushroom is one of the fungi types' foods that has the most powerful nutrients on the plant.
However, there are as yet different mushroom species that have not been identified.
Nevertheless, the identification of edible, non-edible, and poisonous mushrooms among its
existing species is a must due to its high demand for peoples’ everyday meals and major
advantage on medical science. For this purpose, we applied InceptionV3, VGG16, and
Resnet50 on 8190 mushrooms images where the ratio of training and testing data was 8:2.
Contrast Limited Adaptive Histogram Equalization (CLAHE) method was used along with
InceptionV3 to obtain the highest test accuracy. CLAHE is a variant of Adaptive Histogram
Equalization (AHE) which improves the local contrast-enhancing of images and the
definitions of edges in each area of an image by limiting over amplify noise is moderately
similar regions of an image. We show a comparison between contrast-enhanced and
without contrast-enhanced methods. Finally, InceptionV3 has achieved 88.40% accuracy
which is the highest among the rest implemented algorithms.
