Optimizing Lyme Bacterial Disease Identification with Fine-Tuned EfficientnetB0 Model

Lyme disease is a vector-borne infection spread by tick bites, and because of its many clinical presentations and similarities to other illnesses, it can be difficult to diagnose. Erythema migrans, a recognizable rash that, if left untreated, might result in more serious skin issues, is one of the early symptoms. If the infection caused by Lyme disease worsens, it can lead to cognitive, heart-related symptoms. and rheumatologic issues. This might make diagnosis more difficult and affect patient outcomes. To overcome these diagnostic issues, this research looks into the automatic identification of Lyme disease using transfer learning techniques. An automated system for Lyme disease diagnosis that is accurate and has been designed by applying transfer learning on medical and health imaging datasets. This method achieves an outstanding accuracy of 98.55% with minimal loss by using a pretrained EfficientNetB0 model that has been fine-tuned. With a 98% of recall and precision rate, the model performs extremely well. For this category classification job, the dataset on skin diseases has been used. Technology development may greatly increase the accuracy and consistency of Lyme disease diagnosis, resulting in more rapid medication and improved treatment results for patients.