Table 4 MCNN + Softmax classification test results regarding the MCNN + SVM network model, the testing phase involved converting the image data from the test dataset into feature vectors. Subsequently, the well-trained ensemble of five binary SVM classifiers was used to perform predictive classification. The final predicted class was determined by selecting the maximum value among the five computed results. Furthermore, a meticulous analysis of the classification outcomes was conducted using a confusion matrix, thereby facilitating a comprehensive assessment of the model’s performance (shown in Table 5).

From: A novel method based on a multiscale convolution neural network for identifying lung nodules