  DFU is a severe complication of diabetes that can lead to amputation of the
lower limb if not treated properly. Inspired by the 2021 Diabetic Foot Ulcer
Grand Challenge, researchers designed automated multi-class classification of
DFU, including infection, ischaemia, both of these conditions, and none of
these conditions. However, it remains a challenge as classification accuracy is
still not satisfactory. This paper proposes a Venn Diagram interpretation of
multi-label CNN-based method, utilizing different image enhancement strategies,
to improve the multi-class DFU classification. We propose to reduce the four
classes into two since both class wounds can be interpreted as the simultaneous
occurrence of infection and ischaemia and none class wounds as the absence of
infection and ischaemia. We introduce a novel Venn Diagram representation block
in the classifier to interpret all four classes from these two classes. To make
our model more resilient, we propose enhancing the perceptual quality of DFU
images, particularly blurry or inconsistently lit DFU images, by performing
color and sharpness enhancements on them. We also employ a fine-tuned
optimization technique, adaptive sharpness aware minimization, to improve the
CNN model generalization performance. The proposed method is evaluated on the
test dataset of DFUC2021, containing 5,734 images and the results are compared
with the top-3 winning entries of DFUC2021. Our proposed approach outperforms
these existing approaches and achieves Macro-Average F1, Recall and Precision
scores of 0.6592, 0.6593, and 0.6652, respectively.Additionally, We perform
ablation studies and image quality measurements to further interpret our
proposed method. This proposed method will benefit patients with DFUs since it
tackles the inconsistencies in captured images and can be employed for a more
robust remote DFU wound classification.
