Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging

ABSTRACTUltrasound breast image classification plays a crucial role in the early detection of breast cancer, particularly in differentiating benign from malignant lesions. Traditional methods face limitations in feature extraction and global information capture, often resulting in lower accuracy for complex and noisy ultrasound images. This paper introduces a novel ultrasound breast image classification network, C‐TUnet, which combines a convolutional neural network (CNN) with a Transformer architecture. In this model, the CNN module initially extracts key features from ultrasound images, followed by the Transformer module, which captures global context information to enhance classification accuracy. Experimental results demonstrate that the proposed model achieves excellent classification performance on public datasets, showing clear advantages over traditional methods. Our analysis confirms the effectiveness of combining CNN and Transformer modules—a strategy that not only boosts the accuracy and robustness of ultrasound breast image classification but also offers a reliable tool for clinical diagnostics, holding substantial potential for real‐world application.

This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM. Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, accuracy, the positive predictive value (PPV), the negative predictive value (NPV), the F1 score, the precision recall curve (PRC), and heat maps. The final models were compared with the diagnostic performance of conventional CNNs, radiomics models, and two radiologists with specialised breast imaging experience. The best-performing deep learning model, that is, the CBAM-based Xception, achieved an area under the ROC curve (AUC) of 0.970, a sensitivity of 0.848, a specificity of 1.000, and an accuracy of 0.891 on the external test set, which was higher than those of other CNNs, radiomics models, and radiologists. The PRC and the heat maps also indicated the favourable predictive performance of the attention-based CNN model. The diagnostic performance of two radiologists improved with deep learning assistance. Using an attention-based deep learning model based on CESM images can help to distinguishing benign from malignant breast lesions, and the diagnostic performance of radiologists improved with deep learning assistance.

Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus Photographs

Pyramid boundary attention network for breast lesion segmentation in ultrasound images

Wearable electroencephalography (EEG) devices for seizure detection accuracy and reliability are deep learning (DL) applications in the field of epilepsy diagnosis. In this study, we sought to increase the accuracy of seizure detection using advanced DL algorithms on the Children’s Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) EEG database. First, a fully convolutional network (FCN) was trained and assessed using accuracy and recall/precision metrics, and the early stopping technique was used to avoid overfitting. To assess the performance, the FCN was evaluated in terms of various metrics, including accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC)-area under the curve (AUC). In addition, two-dimensional (2D) convolutional neural networks (CNNs) and long short-term memory (LSTM) models were used to model the database, and their performance was thoroughly measured using different metrics, graphs, and confusion matrices. Using LSTM variants, such as standard LSTM, bidirectional LSTM, stacked LSTM, and LSTM attention mechanisms, hybrid convolutional LSTM (ConvLSTM) models were trained and compared. The comparison was conducted based on the training and validation accuracy and loss, as well as the graphs resulting from the precision–recall curves. Apart from DL approaches, EEG signal analysis using time–frequency techniques, such as wavelet transform and short-time Fourier transform, has also been investigated. These methods assisted in the analysis of the time–frequency features of EEG signals in combination with DL models. This study demonstrates that the performance of wearable EEG devices can be augmented using a combination of DL and seizure signal processing techniques. The FCN achieved an accuracy of 92%, with a recall for seizures of 33%, an F1-score of 0.03, and strong ROC-AUC results. The 2D CNN achieved 96% accuracy, a seizure recall of 70%, an F1-score of 0.12, and an ROC-AUC score of 78%. The baseline LSTM struggled with effectiveness at 53% accuracy with a seizure recall of 18%. In contrast, the LSTM model, which incorporated synthetic minority oversampling technique (SMOTE) balancing, was able to reach improvements of up to 89% accuracy, with a precision of 91%, a recall of 86%, an F1-score of 0.89, and a strong ROC curve performance. Among the models, the LSTM with SMOTE was the best performer, with 89% accuracy, 91% precision, 86% recall, and an F1-score of 0.89. These results provide evidence that applying techniques for data balancing in combination with certain DL network architectures significantly improves the detection of seizures using wearable EEG devices worn on the body. We believe that real-time monitoring and high-performance systems are feasible using optimized DL frameworks. The analysis of the performance of different models allows for the understanding of the possibilities of optimizing the architectures of DL algorithms for the modern diagnosis of epilepsy in real time. The source code used to carry out the experiments is publicly available at CHB-MIT EEG Dataset Python Scripts (https://www.kaggle.com/code/adnankust/adnaneeg1).

Breast cancer is one of the deadliest cancer types affecting women worldwide. As with all types of cancer, early detection of breast cancer is of vital importance. Early diagnosis plays an important role in reducing deaths and fighting cancer. Ultrasound (US) imaging is a painless and common technique used in the early detection of breast cancer. In this article, deep learning-based approaches for the classification of breast US images have been extensively reviewed. Classification performance of breast US images of architectures such as AlexNet, VGG, ResNet, GoogleNet and EfficientNet, which are among the most basic CNN architectures, has been compared. Then, transformer models, which are one of the most popular deep learning architectures these days and show similar performance to the performance of CNN' architectures in medical images, are examined. BUSI, the only publicly available dataset, was used in experimental studies. Experimental studies have shown that the transformer and CNN models successfully classify US images of the breast. It has been observed that vision transformer model outperforms other models with 88.6% accuracy, 90.1% precison, 87.4% recall and 88.7% F1-score. This study shows that deep learning architectures are successful in classification of US images and can be used in the clinic experiments in the near future.

Background: Today's machine-learning based dermatologic research has largely focused on pigmented/non-pigmented lesions concerning skin cancers. However, studies on machine-learning-aided diagnosis of depigmented non-melanocytic lesions, which are more difficult to diagnose by unaided eye, are very few.Objective: We aim to assess the performance of deep learning methods for diagnosing vitiligo by deploying Convolutional Neural Networks (CNNs) and comparing their diagnosis accuracy with that of human raters with different levels of experience.Methods: A Chinese in-house dataset (2,876 images) and a world-wide public dataset (1,341 images) containing vitiligo and other depigmented/hypopigmented lesions were constructed. Three CNN models were trained on close-up images in both datasets. The results by the CNNs were compared with those by 14 human raters from four groups: expert raters (>10 years of experience), intermediate raters (5–10 years), dermatology residents, and general practitioners. F1 score, the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity metrics were used to compare the performance of the CNNs with that of the raters.Results: For the in-house dataset, CNNs achieved a comparable F1 score (mean [standard deviation]) with expert raters (0.8864 [0.005] vs. 0.8933 [0.044]) and outperformed intermediate raters (0.7603 [0.029]), dermatology residents (0.6161 [0.068]) and general practitioners (0.4964 [0.139]). For the public dataset, CNNs achieved a higher F1 score (0.9684 [0.005]) compared to the diagnosis of expert raters (0.9221 [0.031]).Conclusion: Properly designed and trained CNNs are able to diagnose vitiligo without the aid of Wood's lamp images and outperform human raters in an experimental setting.

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study

Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and NPK deficiency symptoms of rice (Oryza sativa)

Recent advances in deep learning have significantly transformed medical diagnostics, particularly in dermatology. Accurate skin disease detection and classification areessential for effective treatment and improved patient outcomes. This systematic review examines deep learning approaches, including Convolutional Neural Networks (CNNs) and transfer learning, for automated dermatological diagnosis. Public datasets such as HAM10000 and ISIC play a key role in training robust models; however, challenges including dataset imbalance, disease heterogeneity, and overfitting remain. Techniques such as ensemble learning, attention mechanisms, explainable artificial intelligence, data augmentation, hybrid models, and task-specific loss functions have been shown to enhance accuracy, robustness, and interpretability. This study follows a systematic review methodology in accordance with the PRISMA guidelines. The review synthesizes 17 studies published between 2021 and 2024, highlighting the potential of deep learning to support scalable and reliable dermatological diagnostic systems.

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net.

Early detection of breast cancer and accurate assessment of lesions are key goals of imaging evaluation. Ultrasound is widely used, but its diagnostic performance is influenced by complex image features, noise, and operator experience. Reducing operator dependence and improving accuracy are critical clinical issues. In this retrospective study, 7,025 breast ultrasound images from our center were annotated based on pathology and split into training, validation, and internal test sets (8:1:1). The Dataset of Breast Ultrasound Images was used as the external test set. YOLO-v7 and YOLO-v8 models were trained through transfer learning after data augmentation and balancing the classes. Performance was compared on internal and external test sets and was evaluated against a reader study. YOLO-v7 and YOLO-v8 reached optimal performance at epochs 294 and 135, respectively. YOLO-v7 slightly outperformed YOLO-v8 on the internal test set, while YOLO-v8 achieved higher accuracy, recall, specificity, precision, and F1 score on the external test set. Both models showed significantly higher accuracy, specificity, and precision than the senior radiologist, with YOLO-v8 achieving a significantly higher F1 score. YOLO-v8 demonstrated better generalization due to its anchor-free mechanism and deeper architecture, while YOLO-v7 showed signs of overfitting. Both models outperformed the junior radiologist and approached or exceeded the diagnostic performance of the senior radiologist, indicating potential to assist less experienced readers. YOLO-v7 and YOLO-v8 effectively classified breast lesions. YOLO-v8 showed faster convergence and higher diagnostic efficiency, suggesting strong potential for clinical application.

Colon cancer is one of the highest cancer diagnosis mortality rates worldwide. However, relying on the expertise of pathologists is a demanding and time-consuming process for histopathological analysis. The automated diagnosis of colon cancer from biopsy examination played an important role for patients and prognosis. As conventional handcrafted feature extraction requires specialized experience to select realistic features, deep learning processes have been chosen as abstract high-level features may be extracted automatically. This paper presents the colon cancer detection system using transfer learning architectures to automatically extract high-level features from colon biopsy images for automated diagnosis of patients and prognosis. In this study, the image features are extracted from a pre-trained convolutional neural network (CNN) and used to train the Bayesian optimized Support Vector Machine classifier. Moreover, Alexnet, VGG-16, and Inception-V3 pre-trained neural networks were used to analyze the best network for colon cancer detection. Furthermore, the proposed framework is evaluated using four datasets: two are collected from Indian hospitals (with different magnifications 4X, 10X, 20X, and 40X) and the other two are public colon image datasets. Compared with the existing classifiers and methods using public datasets, the test results evaluated the Inception-V3 network with the accuracy range from 96.5% - 99% as best suited for the proposed framework.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Multivariate time series classification (MTSC) is a fundamental and essential research problem in the domain of time series data mining. Recently deep neural networks emerged as an end-to-end solution for MTSC and achieve state-of-the-art results on several public datasets. It is favored by its hierarchical feature extraction ability and most of the researches focus on designing a network architecture to ensure its performance on MTSC. Despite this, there are seldom investigations on the attention mechanism in MTSC, which has been demonstrated as an effective module to extract features in other domains. In this paper, we propose a residual channel and temporal attention (CT_CAM) module, which aims to refine the feature extracted from the convolutional neural network and thus improve the classification performance. Extensive experiments on 15 public MTSC datasets show that the proposed CT_CAM module achieves competitive performance compared with nine baseline methods and three other attention modules.