Detection of Diabetic Foot Ulcer from Kaggle datasets using LBP and LIPC Methods

Computer Science

The identification of Diabetic Foot ulcers is critical for patients' early diagnosis and therapy planning. Image processing algorithms have developed as useful tools for automatic and reliable ulcer diagnosis from medical imaging data in recent years. This research describes a novel approach for detecting Diabetic Foot ulcers using image processing techniques. A series of preprocessing processes are used in the proposed method to improve the quality of Diabetic Foot pictures and reduce noise. Image scaling, noise reduction, and contrast improvement are all included. Following preprocessing, image segmentation algorithms are used to isolate probable ulcer locations and separate the Diabetic Foot region from the backdrop. Various feature extraction approaches are used to extract significant features from the segmented Diabetic Foot regions for ulcer identification. These criteria capture crucial ulcer properties such as form, texture, and intensity fluctuations. The retrieved features are then used to train a classifier to distinguish between ulcer and non-ulcer regions. Experiments are carried out using a Kaggle dataset of Diabetic Foot pictures encompassing both ulcer and non-ulcer instances to assess the efficacy of the suggested technique. The findings show that the proposed method for detecting Diabetic Foot ulcers is highly accurate and efficient. Comparisons with existing approaches demonstrate the suggested method's advantages in terms of detection accuracy and computing efficiency. Overall, the suggested image-based Diabetic Foot ulcer detection system has significant promise for supporting medical professionals in the early detection of Diabetic Foot ulcers. It has the potential to improve patient outcomes by allowing for timely intervention and personalized treatment regimens.

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