Digital image processing and recognition technology for classification and recognition of hydrothorax cancer cells

Pathological diagnosis is the most common and reliable method of cancer diagnosis, but the technology of pathological diagnosis is relatively backward. It is an urgent problem to identify and classify the pathological pictures of cancer cells. Based on this, the digital image processing and recognition technology are analyzed for the classification and recognition of hydrothorax cancer cells. There is a big difference in the morphology of pleural effusion cancer cells, and uncertainty, so the edge detection algorithm is improved, with the simulated edge detection method used to extract information. After image segmentation, feature extraction is of vital importance for cell image classification. A method of block statistics based on Gabor coefficient is proposed. Firstly, the cell image is filtered by multi-scale and multi-directional filtering, then the average and variance are calculated, and the image is divided into several blocks to solve the problem of large amount of data. Finally, BP neural network is established to input the morphological characteristics of hydrothorax cells, and the results are classified directly. After the experiment, the proposed classification method can improve the classification effectiveness; the design model can accurately identify the breast water cancer cells, and can be effectively applied to the early diagnosis of breast water cancer cells.