An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification

A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer

Cervical cancer is a significant health problem worldwide, and early detection and treatment are critical to improving patient outcomes. To address this challenge, a deep learning (DL)-based cervical classification system is proposed using 3D convolutional neural network and Vision Transformer (ViT) module. The proposed model leverages the capability of 3D CNN to extract spatiotemporal features from cervical images and employs the ViT model to capture and learn complex feature representations. The model consists of an input layer that receives cervical images, followed by a 3D convolution block, which extracts features from the images. The feature maps generated are down-sampled using max-pooling block to eliminate redundant information and preserve important features. Four Vision Transformer models are employed to extract efficient feature maps of different levels of abstraction. The output of each Vision Transformer model is an efficient set of feature maps that captures spatiotemporal information at a specific level of abstraction. The feature maps generated by the Vision Transformer models are then supplied into the 3D feature pyramid network (FPN) module for feature concatenation. The 3D squeeze-and-excitation (SE) block is employed to obtain efficient feature maps that recalibrate the feature responses of the network based on the interdependencies between different feature maps, thereby improving the discriminative power of the model. At last, dimension minimization of feature maps is executed using 3D average pooling layer. Its output is then fed into a kernel extreme learning machine (KELM) for classification into one of the five classes. The KELM uses radial basis kernel function (RBF) for mapping features in high-dimensional feature space and classifying the input samples. The superiority of the proposed model is known using simulation results, achieving an accuracy of 98.6%, demonstrating its potential as an effective tool for cervical cancer classification. Also, it can be used as a diagnostic supportive tool to assist medical experts in accurately identifying cervical cancer in patients.

Classification of benign and malignant pulmonary nodule based on local-global hybrid network

BACKGROUND

The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules.

OBJECTIVE

In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules.

METHODS

First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features.

RESULTS

Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%.

CONCLUSION

The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.