A biological image classification method based on improved CNN

With the increase of biological images, how to classify them effectively is a challenging problem, the Convolutional Neural Networks (CNNs) show promise for this problem. The challenges of using CNNs to handle images classification lie in two aspects: (1) How to further improve the classification accuracy? (2) How to make the network more light weight? To address the above challenges, this paper proposed a biological image classification method based on improved CNN. In this paper, fixed size images as input of CNN are replaced with appropriately large size images and some modules were replaced with an Inverted Residual Block module with fewer computational cost and parameters. The proposed method extensively evaluated the computational cost and classification accuracy on five well known benchmark datasets, and the results demonstrate that compared with existing image classification methods, proposed method shows better performance image classification and reduces the network parameters and computational cost.