Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

Abstract

Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

Liver cancer detection using Artificial Intelligence

“…Chen et al [12] Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism. This study used K-2021050 Male and Female patient's dataset and based on the development of a complete data acquisition scheme.…”

Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention

“…SENet introduces convolution-based attention module which contains channel attention and spatial attention. The results show that the attentional mechanism in SENet plays an excellent role in the histopathological images of liver cancer [ 14 ]. Junlong Cheng proposes an attention module that can capture important features in medical images from channel and spatial dimension.…”

“…In addition, pretrained models [ 13 ] are also used to solve this problem. For the third issue, More and more researchers embed attention mechanism in their models to help them select important features [ 14 ]. Nowadays, these techniques have widely used in many fields, but there is still a gap in the VO and PVD classification task.…”

An approach for liver cancer detection from histopathology images using hybrid pre-trained models

“…The dataset from different tissue categories was employed to pre-train CNNs, and the accuracy was 84.3% vs 78.3%, with an area under the receiver operating characteristic curve (AUC) of 0.918 vs 0.867 [7]. Using the SENet deep learning network, Chen et al [8] demonstrated a 95.27% accuracy in classifying all kinds of distinguished liver cancer histopathology images. The binary classifier developed by Lin et al [9] classified HCC histopathological images with 91.37% accuracy, 92.16% sensitivity, and 90.57% specificity.…”