[Retracted] Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition

Table 1

Cancer prediction tools and methodology.

Tools/methodology	Year	Accuracy	Dataset requirements	Description
Granular computing [8, 14]	2008	100% (stated)	Large	This algorithm eliminates noise and unwanted genes to predict better
Neural network with MRI image [12, 21]	2010	NA	Large	Neurofuzzy classifiers were used on the brain tumour test data
Support vector machine with fuzzy [22, 28, 31]	2011	92%	Medium	It uses liver cancer datasets for testing. Various micro-ranking–level techniques were implemented to classify
Support vector machine with PSO [23, 34–36]	2012	96%	Medium	Uses breast cancer datasets, but for other datasets, the result and accuracy can deviate
ANN with PSO [24, 37–39]	2012	92.36%	Medium	It was used on the tumour cells. Also implemented on the breast cancer datasets
Particle swarm optimization (PSO) integrated with seeker optimization algorithm (SOA) [25]	2013	~93%	Medium	Liver tumours were analysed and classified
Deep learning [26]	2020	NA	Medium	Using multiomics data for cancer classification