Zhenwei Li

Lung cancer (LC) is a malignant tumor that poses a serious threat to human health. Positron emission tomography/computed tomography (PET/CT) imaging, which integrates both anatomical structure and metabolic activity, plays a crucial role in the auxiliary diagnosis of LC. Despite its advantages, the automated classification of LC subtypes is challenged by the overlapping imaging features among different subtypes and the complex morphological characteristics of the lesions. To address the aforementioned challenges, this paper proposes EfficientNet–MobileViT adaptive weighted fusion network, a dual-branch adaptive weight fusion network based on deep learning for LC classification using fused PET/CT images. The method employs a pretrained efficientnet-B1 as the local path to extract detailed structural features, while MobileViT serves as the global path to capture long-range contextual semantic information. A Transformer module is simultaneously introduced to facilitate cross-branch feature interaction. Furthermore, an adaptive weight fusion mechanism is designed to dynamically adjust the contribution of each feature branch during the classification process. Experimental results demonstrate that the proposed method achieves a classification accuracy of 0.9389 on the Lung-PET-CT-DX dataset for the three-class classification task of lung adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. It provides new insights for research on intelligent analysis and computer-aided diagnosis of LC using fused PET/CT images.

Retinal diseases are the leading causes of visual impairment, and early diagnosis is essential for treatment. Optical coherence tomography (OCT), a non-invasive imaging technique, provides high-resolution images for retinal disease classification; however, its complexity and the limitations of manual diagnosis require efficient automated classification methods. To enableearly clinical diagnosis, this paper proposes a Convolutional Block Attention Module (CBAM)- Automatic Mixing Precision (AMP) network (CANet) for automated classification of retinal images. The model categorizes diabetic macular oedema (DME), choroidal neovascularisation (CNV), drusen. and normal cases, which is built upon the ResNet-34 architecture. CBAM is introduced into the residual block to propose the CBAM-Block residual block embedded in the ResNet-34 network model, which combines the channel and spatial attention mechanism to enhance the lesion feature extraction capability. AMP is used to accelerate the training and combine with transfer learning to enhance the model generalization. Meanwhile, median filtering, normalization, dynamic thresholding to remove white edges and data enhancement are used to optimize data quality and alleviate the problem of category imbalance. Classification experiments were performed on the OCT-2017 dataset for the four categories and ablation experiments were performed to demonstrate their effectiveness. The total classification accuracy of the model reaches 0.9890, the AUC value of all categories is 1, where the recall of CNV reaches 1. CBAM, AMP, and transfer learning improve the classification accuracy by 0.9%, 1.6%, and 9.4%, respectively, and the ablation experiments likewise prove that the model remains highly robust to noisy data. The experimental results show that the CANet model significantly improves the OCT image classification performance through multi-module integration, which provides an efficient and reliable technical solution for the automated diagnosis of retinal diseases.

Skin cancer represents a significant global public health concern, with over five million new cases diagnosed annually. If not diagnosed at an early stage, skin diseases have the potential to pose a significant threat to human life. In recent years, deep learning has increasingly been used in dermatological diagnosis. In this paper, a multiclassification model based on the Inception-v2 network and the focal loss function is proposed on the basis of deep learning, and the ISIC 2019 dataset is optimised using data augmentation and hair removal to achieve seven classifications of dermatological images and generate heat maps to visualise the predictions of the model. The results show that the model has an average accuracy of 89.04%, a precision of 87.37%, recall of 90.15%, and an F1-score of 88.76%, The accuracy rates of ResNext101, MobileNetv2, Vgg19, and ConvNet are 88.50%, 85.30%, 88.57%, and 86.90%, respectively. These results show that our proposed model performs better than the above models and performs well in classifying dermatological images, which has significant application value.

Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.

In recent years, significant progress has been made in seizure prediction using machine learning methods. However, fully supervised learning methods often rely on a large amount of labeled data, which can be costly and time-consuming. Unsupervised learning overcomes these drawbacks but can suffer from issues such as unstable training and reduced prediction accuracy. In this paper, we propose a semi-supervised seizure prediction model called WGAN-GP-Bi-LSTM. Specifically, we utilize the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as the feature learning model, using the Earth Mover’s distance and gradient penalty to guide the unsupervised training process and train a high-order feature extractor. Meanwhile, we built a prediction model based on the Bidirectional Long Short-Term Memory Network (Bi-LSTM), which enhances seizure prediction performance by incorporating the high-order time-frequency features of the brain signals. An independent, publicly available dataset, CHB-MIT, was applied to train and validate the model’s performance. The results showed that the model achieved an average AUC of 90.08%, an average sensitivity of 82.84%, and an average specificity of 85.97%. A comparison with previous research demonstrates that our proposed method outperforms traditional adversarial network models and optimizes unsupervised feature extraction for seizure prediction.

Fundus diseases cause damage to any part of the retina. Untreated fundus diseases can lead to severe vision loss and even blindness. Analyzing optical coherence tomography (OCT) images using deep learning methods can provide early screening and diagnosis of fundus diseases. In this paper, a deep learning model based on Swin Transformer V2 was proposed to diagnose fundus diseases rapidly and accurately. In this method, calculating self-attention within local windows was used to reduce computational complexity and improve its classification efficiency. Meanwhile, the PolyLoss function was introduced to further improve the model’s accuracy, and heat maps were generated to visualize the predictions of the model. Two independent public datasets, OCT 2017 and OCT-C8, were applied to train the model and evaluate its performance, respectively. The results showed that the proposed model achieved an average accuracy of 99.9% on OCT 2017 and 99.5% on OCT-C8, performing well in the automatic classification of multi-fundus diseases using retinal OCT images.

Melanoma is a fatal skin cancer that can be treated efficiently with early detection. There is a pressing need for dependable computer-aided diagnosis (CAD) systems to address this concern effectively. In this work, a melanoma identification method with feature interpretation was designed. The method included preprocessing, feature extraction, feature ranking, and classification. Initially, image quality was improved through preprocessing and k-means segmentation was used to identify the lesion area. The texture, color, and shape features of this region were then extracted. These features were further refined through feature recursive elimination (RFE) to optimize them for the classifiers. The classifiers, including support vector machine (SVM) with four kernels, logistic regression (LR), and Gaussian naive Bayes (GaussianNB) were applied. Additionally, cross-validation and 100 randomized experiments were designed to guarantee the generalization of the model. The experiments generated explainable feature importance rankings, and importantly, the model demonstrated robust performance across diverse datasets.

Accurate segmentation of different brain tumor regions from MR images is of great significance in the diagnosis and treatment of brain tumors. In this paper, an enhanced 3D U-Net model was proposed to address the shortcomings of 2D U-Net in the segmentation tasks of brain tumors. While retaining the U-shaped characteristics of the original U-Net network, an enhanced encoding module and decoding module were designed to increase the extraction and utilization of image features. Then, a hybrid loss function combining the binary cross-entropy loss function and dice similarity coefficient was adopted to speed up the model’s convergence and to achieve accurate and fast automatic segmentation. The model’s performance in the segmentation of brain tumor’s whole tumor region, tumor core region, and enhanced tumor region was studied. The results showed that the proposed 3D U-Net model can achieve better segmentation performance, especially for the tumor core region and enhanced tumor region tumor regions.

Timely detection of dynamic and static obstacles and accurate identification of signal lights using image processing techniques is one of the key technologies for guidance robots and is a necessity to assist blind people with safe travel. Due to the complexity of real-time road conditions, current obstacle and traffic light detection methods generally suffer from missed detection and false detection. In this paper, an improved deep learning model based on YOLOv5 is proposed to address the above problems and to achieve more accurate and faster recognition of different obstacles and traffic lights that the blind may encounter. In this model, a coordinate attention layer is added to the backbone network of YOLOv5 to improve its ability to extract effective features. Then, the feature pyramid network in YOLOv5 is replaced with a weighted bidirectional feature pyramid structure to fuse the extracted feature maps of different sizes and obtain more feature information. Finally, a SIoU loss function is introduced to increase the angle calculation of the frames. The proposed model’s detection performance for pedestrians, vehicles, and traffic lights under different conditions is tested and evaluated using the BDD100K dataset. The results show that the improved model can achieve higher mean average precision and better detection ability, especially for small targets.

At present, multi-disease fundus image classification tasks still have the problems of small data volumes, uneven distributions, and low classification accuracy. In order to solve the problem of large data demand of deep learning models, a multi-disease fundus image classification ensemble model based on gradient-weighted class activation mapping (Grad-CAM) is proposed. The model uses VGG19 and ResNet50 as the classification networks. Grad-CAM is a data augmentation module used to obtain a network convolutional layer output activation map. Both the augmented and the original data are used as the input of the model to achieve the classification goal. The data augmentation module can guide the model to learn the feature differences of lesions in the fundus and enhance the robustness of the classification model. Model fine tuning and transfer learning are used to improve the accuracy of multiple classifiers. The proposed method is based on the RFMiD (Retinal Fundus Multi-Disease Image Dataset) dataset, and an ablation experiment was performed. Compared with other methods, the accuracy, precision, and recall of this model are 97%, 92%, and 81%, respectively. The resulting activation graph shows the areas of interest for model classification, making it easier to understand the classification network.