The chorioallantoic membrane (CAM) model resembles a preclinical in vivo platform for the engraftment of different tumors and is especially well-suited for the development and evaluation of novel cancer therapies. Due to the CAM's extensive vascular network, it can sustain engrafted human tumor tissue, which can be used for therapy testing and analysis throughout the experimental process. Although numerous studies have demonstrated successful tumor engraftment and vascular integration into the CAM's blood vessel network, the accurate and sequential measurement of tumor volume remains a significant challenge. Current imaging modalities, such as positron emission tomography/computed tomography, magnetic resonance imaging or three-dimensional (3D) digital microscopy are limited by cost, complexity, and potential harm due to injected agents or radiation. This study introduces an innovative imaging approach by utilizing ultrahigh frequency (UHF) ultrasound combined with 3D ultrasound reconstruction of pancreatic ductal adenocarcinoma (PDAC) on the CAM. By utilizing ultrasound frequencies of up to 71 MHz, two-dimensional (2D) imaging of PDAC tumors was transformed into comprehensive 3D reconstructions. This innovative approach provides a more detailed tissue resolution and enables noninvasive, longitudinal volumetric monitoring of tumor growth in vivo, and in principle provides a platform for future studies assessing angiogenesis and therapy response. In this study the accuracy of 3D UHF ultrasound was tested by comparing it with established imaging techniques such as 3D digital microscopy. In summary, the 3D ultrasound reconstruction resembles a more versatile noninvasive alternative for in vivo tumor volume measurement. This advanced imaging approach adds a valuable methodology for the investigation of tumor biology in the CAM model by providing noninvasive volumetric and morphological characterization of engrafted tumor tissue, thereby linking preclinical research and clinical applications.
During the COVID-19 pandemic, there were many concerns raised in diagnosing chest diseases. Regular diagnosis processes of chest diseases sometimes fail in distinguishing between Corona, viral pneumonia diseases, and normal chest x-rays through regular assessments which also need time and not as accurate as the automated diagnosis based on machine learning. Chest diseases can be sometimes diagnosed using x-ray images where these images are used to identify if the patient is normal or has one of the chest diseases like Corona and viral pneumonia. Modern machine learning techniques have achieved high performance in aiding medical diagnostic processes. The novelty of this work lies in a wavelet-based feature extraction framework coupled with a systematic evaluation of ensemble learning algorithms for accurate and computationally efficient classification of COVID-19, viral pneumonia, and normal chest x-ray images. The number of patients used in each case is 1200 patients and this huge dataset is used to create classification models using adaptive boosting (AdaBoost), gradient boosting (GB), and random forest (RF) machine learning algorithms using Orange3 data mining tool. Using wavelet features, the results of evaluating the proposed models were 99.5% on average. The proposed models are very effective in identifying chest diseases and can be combined with x-ray machines in hospitals, so physicians can determine the best treatment for patients. The proposed methods beat other researchers found in literature as they are faster and more accurate where each step takes few seconds on average to complete.
Ultrasound imaging is a widely used diagnostic tool, and beamforming techniques are integral to its performance. Traditional methods such as delay-and-sum (DAS) and delay-multiply-and sum (DMAS) have limitations in terms of image quality and computational efficiency. Recently, deep learning approaches have shown significant promise in improving ultrasound beamforming, offering enhanced image resolution and quality, alongside potential for real-time processing. This review is aimed at providing a comprehensive overview of the current state-of-the-art in deep learning–based ultrasound beamforming. It focuses on analyzing various deep learning models, their applications, challenges, and the integration of hardware optimization strategies to enhance performance and efficiency. The review examines a range of deep learning models applied to ultrasound beamforming, including convolutional neural networks (CNNs), generative adversarial networks (GANs), transformer-based models, and hybrid approaches. It also discusses the challenges associated with dataset limitations, model interpretability, and the risk of overfitting. Furthermore, the review explores hardware acceleration using field-programmable gate arrays (FPGAs), along with cloud-edge frameworks for real-time inference. The findings highlight the potential of deep learning models to outperform traditional methods in terms of image quality and resolution. However, challenges such as the need for large and diverse datasets, the black box nature of deep learning models, and the risk of overfitting remain. Hardware optimization through FPGAs has proven effective in enabling real-time processing, but the integration of edge computing with cloud-based solutions offers promising avenues for balancing performance, latency, and energy efficiency. Deep learning–based ultrasound beamforming has great potential to advance medical imaging. Future efforts should enhance model generalizability with diverse and synthetic data, optimize hardware for real-time use, and establish standardized validation protocols to support clinical adoption.
The paper presents a framework that is aimed at addressing the large-scale data challenge with a particular focus on early prediction of breast cancer based on mammography image data. The abundance of large-scale medical imaging data in healthcare has opened the way to intelligent diagnostic systems through its ability to analyze and process large-scale mammography data. The proposed framework, the metaheuristic optimization based large-scale breast cancer detection (MO-LSBCD) framework provides a complete solution to the extraction of discriminative features that can be used to predict the disease correctly. Early and accurate diagnosis of breast cancer is one of the most critical factors in improving survival rates and reducing treatment costs for patients. The MO-LSBCD framework is divided into three phases: (1) The CBIS-DDSM mammography dataset consists of 10,239 image files with a size of 6 GB, but for classification, the analytical unit is the annotated region of interest (ROI), with 3568 ROI-level abnormality records from 1566 unique patients (normal, malignant, benign, and benign-without-callback); (2) solving the large-scale data problem by implementing an optimized MapReduce model to efficiently distribute and process the large volumes of breast cancer mammography images in parallel; and (3) effective deep learning techniques to classify breast cancer pathology. The experimental findings indicate that the enhanced MapReduce model reduces total processing time and improves F-measure in comparison with the standard MapReduce model. Among different deep learning models tested on the CBIS-DDSM dataset, the classification of EfficientNet-B3 and ResNet-50 was better with an accuracy of 99.64% and 98.91%, respectively, compared with the other models on all evaluation metrics. The results of this paper confirm that MO-LSBCD framework can be used to provide a robust and scalable solution to large-scale breast cancer detection to help clinicians in the early diagnosis of breast cancer and eventually help in the improved patient outcome.
Optical coherence tomography (OCT) is an important imaging modality for detecting retinal disorders, including diabetic macular edema (DME), choroidal neovascularization (CNV), and drusen, as well as distinguishing healthy retinal patterns. Nevertheless, visual assessment of OCT scans is labor-intensive, observer-dependent, and dependent on expert clinical knowledge, encouraging reliable automated screening tools. This work introduces a hybrid Deep Transferred EfficientNet and Support Vector Machine (DTE-SVM) approach for multiclass OCT image classification. In contrast to standard CNN models that commonly use a softmax layer for final prediction, the proposed framework employs transfer-learned EfficientNet representations with an SVM decision model to strengthen interclass discrimination and robustness within the extracted feature space. Experiments used a working subset of 8767 OCT images selected from the OCT2017 dataset, representing nearly 10.4% of the complete 84,495-image collection. This smaller yet representative subset was adopted to lower computational cost while maintaining class distribution among CNV, DME, drusen, and normal groups. Five EfficientNet architectures, from B0 through B4, were assessed using stratified 10-fold cross-validation. Among them, DTE-SVM-B0 produced the strongest overall results, obtaining an accuracy of 0.9076, precision of 0.9074, recall of 0.9076, and F1-score of 0.9073. DTE-SVM-B2 showed the smallest fold-level standard deviation, reflecting comparatively stable behavior. Comparative experiments indicated that DTE-SVM-B0 surpassed ResNet50, DenseNet-121, and EfficientNetB0 softmax-based classifiers, whereas ablation testing verified that substituting the softmax layer with SVM improved results for every EfficientNet backbone. Class-level clinical evaluation also revealed strong sensitivity and specificity, with CNV identified most accurately and drusen remaining the hardest category. Overall, these results suggest that compact EfficientNet feature extractors integrated with SVM offer accurate, stable, and computationally practical OCT-driven retinal disease classification for clinical decision-support settings.
