Enhanced Nail Disease Recognition through DenseNet121: A Novel Deep Learning Framework

Detection of nail diseases, particularly those conditions such as Acral Lentiginous Melanoma, forms the vital basis for early diagnosis and treatment. The visual similarities in different nail diseases often make manual diagnoses problematic and riddled with errors. This research is focused on the application of deep learning in enhancing the efficiency and effectiveness of nail disease detection. The dataset is diversified, as it is drawn from six classes of images: Acral Lentiginous Melanoma, Healthy Nail, Onychogryphosis, Blue Finger, Clubbing, and Pitting. Each class in this dataset contributes to a different nail condition, therefore giving a very solid foundation when it comes to the training of machine learning models. Each of the images is a JPEG image, ensuring a wide compatibility with various platforms and tools. The goal is to develop an automated nail disease classification system that can assist in early diagnosis. Based on the above, diagnostic tools can be developed which will help the medical professional diagnose which diseases are hard to recognize in the nails. Thus, the model is subjected to evaluation against goodwill in order to classify images within the mentioned categories, reaching a final validation accuracy of 86% and finding with a validation loss of 0.4589. These results really show the potential of deep learning in medical image analysis and, therefore, form a very promising solution to improving diagnostic accuracy with regard to nail disease diagnosis. The dataset is also of great educational value, forming a resource for medical students and researchers interested in becoming acquainted with the visual appearance of nail conditions.