Inception v3 based cervical cell classification combined with artificially extracted features

Abstract Traditional cell classification methods generally extract multiple features of the cell manually. Moreover, the simple use of artificial feature extraction methods has low universality. For example, it is unsuitable for cervical cell recognition because of the complexity of the cervical cell texture and the large individual differences between cells. Using the convolutional neural network classification method is a good way to solve this problem. However, although the cell features can be extracted automatically, the cervical cell domain knowledge will be lost, and the corresponding features of different cell types will be missing; hence, the classification effect is not sufficiently accurate. Aiming at addressing the limitations of the two mentioned classification methods, this paper proposes a cell classification algorithm that combines Inception v3 and artificial features, which effectively improves the accuracy of cervical cell recognition. In addition, to address the under-fitting problem and carry out effective deep learning training with a relatively small amount of medical data, this paper inherits the strong learning ability from transfer learning, and achieves accurate and effective cervical cell image classification based on the Herlev dataset. Using this method, an accuracy of more than 98% is achieved, providing an effective framework for computer aided diagnosis of cervical cancer. The proposed algorithm has good universality, low complexity, and high accuracy, rendering it suitable for further extension and application to the classification of other types of cancer cells.