Classification of Cell Diffraction Images Based on Deep Neural Network

With the continuous advancement of the life sciences and the advancement of ultra-high resolution technology, people can observe biological information such as organelles and molecules, and can understand their internal structure and interaction from the acquired cell diffraction images. However, cells contain various types of organelles, which have high heterogeneity, and the cell structures of different types of cells have certain differences. Therefore, the study of cell diffraction image classification is of great significance in many fields such as cell morphology and cell biology. The research task of image classification is to extract useful feature information from the image, and then distinguish the images of different attributes, and finally divide the image targets of different categories. Deep learning techniques are used in a variety of industries, including picture categorization, as a result of the development of deep learning. Among these, there has been a notable improvement in the accuracy of picture classification using deep neural networks. The classification accuracy can be further increased in the real cell diffraction image classification procedure, though. This study proposes a deep neural network-based classification strategy for cell diffraction pictures. For the cell diffraction images, some interference pictures are created by cell debris or impurities. In this work, the produced diffraction pictures are preprocessed using a clustering approach and a support vector machine (SVM).After that, a Gray Level Co-occurrence Matrix(GLCM) is used to extract the texture features of the diffraction image. This research proposes an enhanced deep neural network-based picture classification algorithm. Max-margin Minimum Classification Error (M3CE) is introduced during the training of deep neural networks, and cross-entropy is used to build the loss function. Finally, the Ramos and Jurkat cell experiments' findings support the high precision of the categorization approach.