Midmark (United States)

Automated leukemia detection from microscopic blood cell images plays a crucial role in hematological diagnostics, enabling early and accurate disease identification. Traditional deep learning (DL) models primarily rely on single-view feature extraction, making them susceptible to variations in staining intensity, illumination, and imaging artifacts. These limitations hinder classification robustness and generalization, posing challenges in real-world clinical applications. To address these issues, this work introduces a multi-view feature fusion framework that integrates raw images, optical density (OD) transformed images, and anisotropically diffused images to enhance feature representation. A cross-attention mechanism is incorporated to adaptively refine salient features while suppressing redundant information across views. Experimental results on a high-resolution leukemia dataset demonstrate that the proposed model achieves a test accuracy of 97.90%, significantly outperforming single-view approaches. Furthermore, an ablation study confirms the impact of OD transformation and anisotropic diffusion in improving classification robustness. The confusion matrix and ROC analysis validate the model’s effectiveness, while LIME-based visual explanations enhance interpretability. These findings highlight the potential of multi-view learning in hematopathology, paving the way for more reliable AI-assisted leukemia diagnostics.

Pneumonia is a severe respiratory infection that presents considerable health hazards globally, particularly in developing nations where insufficient medical resources sometimes impede early detection. Recent advancements in deep learning, particularly convolutional neural networks, have demonstrated significant potential in the automated diagnosis of pneumonia from chest X-rays. This could be a cost-effective and accurate solution for healthcare settings that do not have many resources. In order to accurately classify pneumonia cases, this paper proposes a custom CNN model that addresses common issues such as class imbalance and overfitting. The study fixed the problem of class imbalance by making the majority class (PNEUMONIA) the same size as the minority class (NORMAL). This ensured that the training dataset was balanced, which helped the model generalize. Our suggested model has a sequential CNN architecture with four Conv layers, including ReLU activation, batch normalization, and MaxPooling layers. These are followed by fully connected layers, making feature extraction and classification work well. Dropout layers are applied in the deeper Conv and fully connected layers to prevent overfitting, ensuring the model generalizes well to unseen data. The Adam optimizer enhances the model via a binary cross-entropy loss function, while EarlyStopping and ModelCheckpoint oversee training to retain the optimal model, reducing overfitting and improving performance. The model underwent training on a balanced dataset for 20 epochs, with a maximum training accuracy of 98.03% and a validation accuracy of 99.78%. This demonstrates a significant improvement over existing models in the literature, highlighting the robustness and reliability of our approach.

Rebecca Watson, MSc RVN, clinical specialist EMEA for Midmark Animal Health, examines how, when and why we bandage our horses' legs

<div class="htmlview paragraph">This paper describes a system for real-time smoke detection in diesel engines. Preliminary results are presented from a very simple sensor which detects the net charge level on smoke particles. There appears to be a useful correlation between the peak charge level and the Bosch smoke number. The mechanism by which the particulates is discussed, though no firm conclusions are reached.</div>

LMI will be introducing its next generation LISP Machine, the Lambda, for market in the spring. The major design philosophy of the Lambda machine is the concept of modularity. The system is designed so that it can exist in a large number of configurations centered around a 32 bit high performance bus.