Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals

13AI and Transfer Learning–Based Framework for Efficient Classification and Detection of Lyme Disease

Pramit Brata Chanda^1*, Saikat Das², Sharanya Bhattacharya², Souhardya Biswas² and Subir Kumar Sarkar¹

¹Electronics and Tele Communication Engineering, Jadavpur University, Jadavpur, West Bengal, India

²Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India

Abstract

Lyme disease has become a vector-borne Skin disease throughout the United States, Europe, and some parts of Asia. This requires proper medical examination of symptoms and other diagnoses to detect the disease and, if left untreated, regions can spread to other major organs of the human body. In this work, the analysis is performed on different images of Lyme skin infections that are caused by specific groups of ticks, to build several transfer learning– and artificial intelligence–based frameworks for predicting if the infection is Lyme or not. Today, artificial intelligence has a crucial role in predicting diseases and reducing human error. The AI-based transfer learning framework is very effective for predicting Lyme disease with good accuracy. Here, a sequential model is created with transfer learning models in Keras and lightweight machine learning models in Scikit Learn which are trained on the dataset of images and assessed the performance and accuracy. As the volume of the medical images collected is quite small, we have experimented with other deep learning ...