• B.Eswar Badri
• A.Karteek
• G.Hemanth
• B.Uday Kiran
• T.Geetamma

The development of open wounds on the feet of people with diabetes, known as diabetic foot ulcers (DFUs), is a frequent complication of the disease. Excess delay of treatment may lead to the risk of foot amputation. The traditional practice of medication of DFUs which includes CT Scan X-Ray or MRI Scan may provide inaccurate, delayed results and high cost of treatment. This paper proposes a model to classify the DFUs as normal skin(unaffected skin) and abnormal skin(DFU affected skin) using Deep Leaning techniques. An extensive dataset images of foot of different patients is considered and convolution neural network(CNN) architecture EfficientNet is used to uniformly scale the depth width resolution of the image with compound coefficient EfficientNet gives more accurate and faster results compared to other convolutional neural networks. The proposed model achieved more accurate results for the given image dataset.

Diabetic Foot Ulcer, Neural Networks, EfficientNet B0, Deep Learning.

"Classification of Diabetic Foot Ulcer using EfficientNet B0", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.i41-i46, March-2023, Available :http://www.jetir.org/papers/JETIR2303807.pdf

"Classification of Diabetic Foot Ulcer using EfficientNet B0", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppi41-i46, March-2023, Available at : http://www.jetir.org/papers/JETIR2303807.pdf