Due to the lack of uniform standards for pathological cell detection, it is difficult to identify. In order to improve the accuracy of pathological cell identification, this study combines the actual situation of cell detection based on traditional particle algorithm to construct a C-V model based on level set algorithm and curve evolution theory, which realizes the effective separation of different substances inside the cell. At the same time, in order to effectively extract the characteristics of cell images, this paper uses the global research method to extract the features of the research object and adopts the improved gray level co-occurrence matrix to extract the texture features, thus effectively improving the feature extraction quality. In addition, in order to study the accuracy of the algorithm model identification in this study, this paper designs a comparative experiment for performance analysis. The research shows that the proposed algorithm model has good performance, can achieve accurate recognition and feature extraction of pathological cells, has certain practical effects, and can provide theoretical reference for subsequent related research.
WuJ., LongJ., LiuM., Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm, Neurocomputing148 (2015), 136–142.
2.
El-EmamN.N., New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization, Computers & Security55 (2015), 21–45.
3.
LiaoS.H., HsiehJ.G., ChangJ.Y., et al., Training neural networks via simplified hybrid algorithm mixing Nelder– Mead and particle swarm optimization methods, Soft Computing19(3) (2015), 679–689.
4.
YadavN., YadavA., KumarM., et al., An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem, Neural Computing and Applications28(1) (2017), 171–178.
5.
PulidoM., MelinP., Optimization of ensemble neural networks with fuzzy integration using the particle swarm algorithm for time series prediction, Intelligent Automation & Soft Computing20(3) (2015), 403–418.
6.
KulatilakeP.H.S.W., QiongW., HudaverdiT., et al., Mean particle size prediction in rock blast fragmentation using neural networks, Engineering Geology114(3–4) (2010), 298–311.
7.
HeX., GuanH., QinJ., A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall, Journal of Hydrology527 (2015), 88–100.
8.
LuY., ZengN., LiuY., et al., A hybrid wavelet neural network and switching particle swarm optimization algorithm for face direction recognition, Neurocomputing155 (2015), 219–224.
9.
BeheshtiZ., FirouziM., ShamsuddinS.M., et al., A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network, Neural Computing and Applications27(8) (2016), 2551–2565.
10.
AalizadehB., AsnafiA., Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions, Neural Computing & Applications4 (2016), 1–9.
11.
JianfangC., HongyanC., HaoS., et al., Big data: A parallel particle swarm optimization-back-propagation neural network algorithm based on mapreduce, Plos One11(6) (2016), e0157551.
12.
LuJ., HuH., BaiY., Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm, Neurocomputing152 (2015), 305–315.
13.
ShadmandS., MashoufiB., A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization, Biomedical Signal Processing and Control25 (2016), 12–23.
14.
SbarufattiC., CorbettaM., GiglioM., et al. Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks, Journal of Power Sources344 (2017), 128–140.
15.
LiuT., YinS., An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation, Multimedia Tools and Applications76(9) (2017), 11961–11974.
16.
NasimiR., IraniR., Combining a neural network with a genetic algorithm and particle swarm optimization for permeability estimation of the reservoir, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects37(4) (2015), 384–391.
17.
DangT.L., HoshinoY., Hardware/software co-design for a neural network trained by particle swarm optimization algorithm, Neural Processing Letters5 (2018), 1–25.
18.
Ghaffari RazinM.R. and VoosoghiB., Ionosphere tomography using wavelet neural network and particle swarm optimization training algorithm in Iranian case study, GPS Solutions21(3) (2017), 1301–1314.
19.
FeiY., Wen-BoD., Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data, Plos one12(12) (2017), e0188746.
20.
YuS., WangK., WeiY.M., A hybrid self-adaptive Particle Swarm Optimization– Genetic Algorithm– Radial Basis Function model for annual electricity demand prediction, Energy Conversion and Management91 (2015), 176–185.
21.
WangH., YanX., Optimizing the echo state network with a binary particle swarm optimization algorithm, Knowledge-Based Systems86 (2015), 182–193.
22.
MoazzamiM., KhodabakhshianA., HooshmandR.A., A new optimal under-frequency load-shedding method using hybrid culture– particle swarm optimization– co-evolutionary algorithm and artificial neural networks, Electric Machines & Power Systems43(1) (2015), 14–21.
23.
Modeling engine fuel consumption and NOx with RBF neural network and MOPSO algorithm, International Journal of Automotive Technology16(6) (2015), 1041–1049.
24.
LiX., XiangS., ZhuP., et al., Establishing a dynamic self-adaptation learning algorithm of the BP neural network and its applications, International Journal of Bifurcation and Chaos25(14) (2015), 1540030.
25.
MoteghaedN.Y., MaghooliK., PirhadiS., et al., Biomarker discovery based on hybrid optimization algorithm and artificial neural networks on microarray data for cancer classification, J Med Signals Sens5(2) (2015), 88–96.
