Diabetic foot ulcers (DFU) are one of the major health complications for people with diabetes. It may cause limb amputation or lead to life-threatening situations if not detected and treated properly at an early stage. A diabetic patient has a 15–25% chance of developing DFU at a later stage in his or her life if proper foot care is not taken. Because of these high-risk factors, patients with diabetes need to have regular checkups and medications which cause a huge financial burden on both the patients and their families. Hence, the necessity of a cost-effective, re-mote, and fitting DFU diagnosis technique is imminent. This paper presents a convolutional neural network (CNN)-based approach for the automated detection of diabetic foot ulcers from the pictures of a patient’s feet. ResNet50 is used as the backbone of the Faster R-CNN which performed better than the original Faster R-CNN that uses VGG16. A total of 2000 images from the Diabetic Foot Ulcer Grand Challenge 2020 (DFUC2020) dataset have been used for the experiment. The proposed method obtained precision, recall, F1-score, and mean average precision of 77.3%, 89.0%, 82.7%, and 71.3%, respectively, in DFU detection which is better than results obtained by the original Faster R-CNN.