Deep learning recognition of diseased and normal cell representation

Cell classification refers to detecting normal and diseased cells from small amount of data. Sometimes, classification of cells becomes difficult because some cells fall into more than one categories/classes. Current state-of-the-art cell classification methods have been developed on the bases of tumor cell classification but these methods cannot classify diseased or normal cells. This study investigated the performance of two classification methods traditional machine learning and deep learning (normal and diseased cell classification) to categorize normal and diseased cells. Millions of normal cells undergo controlled growth and uncontrolled growth may be involved in disease causation but their clinical applications remain limited due to difficulties in distinguishing normal and diseased cells. Previous studies are limited to identify, systematically, the normal and diseased cells. This study collected information about diseased or normal cells to check the networks for correct cell detection and then eliminated false-positive cells. We used machine learning methods along with logistic regression, support vector machine, and CNN (convolutional neural network). We found that our proposed method classified better the normal and diseased cells. With the help of two types of images: normal and diseased cells, we trained a CNN that identified diseased cells with 98% accuracy and enabled the discovery of normal and diseased cells. As a result, it will advance the clinical utility of human diseased cells.