Abstract
Medical image processing is essential for identifying conditions like skin cancer, brain tumors, breast cancer, and illnesses of the heart and lungs. To detect disorders, the human nail serves as a window into the body. Various methods are available in the healthcare industry for early disease diagnosis. One method for diagnosing diseases in their early stages is nail image analysis. It is possible to forecast some general and dermatological disorders using human nails. The naked human eye cannot detect numerous variations in nail color or texture. In this research, models for illness prediction using nail pictures are constructed. In this work, the fusion of statistical and structural texture featuresis employed to forecast diseases. Merging structural and statistical features allows for the analysis of the correctness of many models and the selection of the most relevant one. Numerous methods, particularly GLCM, LBP, and Gabor filter, were used to combine statistical and structural features
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