Fusion model of gray level co-occurrence matrix and convolutional neural network faced for histopathological images

The image recognition of cancer cells plays an important role in diagnosing and treating cancer. Deep learning is suitable for classifying histopathological images and providing auxiliary technology for cancer diagnosis. The convolutional neural network is employed in the classification of histopathological images; however, the model's accuracy may decrease along with the increase in network layers. Extracting appropriate image features is helpful for image classification. In this paper, different features of histopathological images are represented by extracting features of the gray co-occurrence matrix. These features are recombined into a 16 × 16 × 3 matrix to form an artificial image. The original image and the artificial image are fused by summing the softmax output. The histopathological images are divided into the training set, validation set, and testing set. Each training dataset consists of 1500 images, while the validation dataset and test dataset each consist of 500 images. The results indicate that the effectiveness of our fusion model is demonstrated through significant improvements in accuracy, precision, recall, and F1-score, with an average accuracy reaching 99.31%. This approach not only enhances the classification performance of tissue pathology images but also holds promise for advancing computer-aided diagnosis in cancer pathology.