Classification of Microglial cells using Deep learning techniques

Microglia are specialized immune cells found only in the Central Nervous System (CNS), with 1.5 trillion numbers of them identified across the spinal cord and brain. These cells display various characteristics depending on the signals they receive, ranging from beneficial to harmful, as evidenced by their movement and physical changes. Numerous neurological illnesses, including Parkinson’s and Alzheimer’s disease, have been connected to changes in the inflammatory condition. Understanding the significance of microglial cells in disease progression and examining the feasibility of novel therapy methods in such situations. In this paper, a study is conducted on the microscopy image of microglial cells. Initially, the image is preprocessed using image processing algorithms, and the number of cells in the image is also automatically counted using the image processing algorithm. The preprocessed image is classified using Deep Learning Neural Network (DNN) to classify whether the cell is in a normal or activated state. The classifiers such as Resnet50, VGG16, VGG 19 and Xception are applied to the preprocessed image. The classifier’s performance is assessed using evaluation parameters. The results are compared to choose the model with the highest accuracy. For this data the accuracy of VGG16 net achieved is 96% which is higher than other model considered.