Microelectromechanical systems (MEMS)-based filter with microchannels enables the removal of various microorganisms, including viruses and bacteria, from fluids. Membranes with porous channels can be used as filtration interfaces in MEMS hemofilters or mini-dialyzers. The main problems associated with the filtration process are optimization of membrane geometry and fouling. A nanoporous aluminum oxide membrane was fabricated using an optimized two-step anodization process. Computational strength modeling and analysis of the membrane with specified parameters were performed using the ANSYS structural module. A fuzzy simulation was performed for the numerical analysis of flux through the membrane. The membrane was then incorporated with the prototype for successive filtration. The fluid flux and permeation analysis of the filtration process have been studied. Scanning electron microscope (SEM) micrographs of membranes have been obtained before and after the filtration cycles. The SEM results indicate membrane fouling after multiple cycles, and thus the flux is affected. This type of fabricated membrane and setup are suitable for the separation and purification of various fluids. However, after several filtration cycles, the membrane was degraded. It requires a prolonged chemical cleaning. High-density water has been used for filtration purposes, so this MEMS-based filter can also be used as a mini-dialyzer and hemofilter in various applications for filtration. Such a demonstration also opens up a new strategy for maximizing filtration efficiency and reducing energy costs for the filtration process by using a layered membrane setup.

The U-Net model for lung area segmentation in medical images is considered in this work. A preprocessed and enhanced dataset was used to train the U-Net model across 70 epochs using an encoder-decoder architecture with skip connections. To estimate the accuracy of the model, important performance indicators such as the Dice coefficient, Intersection over Union (IOU), and training/validation loss were observed. The coefficient for the dice went up from 0.5 to 0.9 in the results, while the IOU value calmed at 0.9, representing the model’s efficiency in proper segmentation. Strong generalization to previously unseen data and minimal overfitting were shown by the loss metrics’ in accordance reduction. In agreement to the study, U-Net has the potential to be used in real-world medical applications. Further study is recommended to enhance performance through the application of transfer learning and consideration devices.

Medical imaging is a critical tool for diagnosing and treating various diseases such as Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, lung cancer, and Coronavirus. Techniques such as X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET) play essential roles in identifying the physical and functional aspects of the lungs. Manual lung segmentation by radiologists, while adjustable, is time-consuming and subject to variability. Consequently, automated lung segmentation methods utilizing Machine Learning (ML) and Deep Learning (DL) have emerged as essential alternatives. This review highlights advancements in automated lung segmentation, focusing on traditional ML methods and state-of-the-art DL approaches, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). While these techniques hold great promise, challenges remain, such as the need for annotated datasets, computational demands, and integration into clinical workflows. This paper explores current applications, identifies challenges, and outlines future opportunities for improving the precision and efficiency of lung segmentation through interdisciplinary collaboration in medical imaging, computer science, and clinical practice.

Accurate brain tumor classification from MRI scans is critical for effective diagnosis and treatment planning. This study compares the performance of three deep learning models—Simple CNN, ResNet50, and InceptionV3—in classifying brain tumors using MRI images. Among the models, InceptionV3 outperformed others, achieving the highest accuracy and generalization to unseen data, making it the most suitable for real-world applications. ResNet50 displayed competitive performance but showed overfitting tendencies, while the Simple CNN served as a baseline model with limited complexity and accuracy. Future improvements, including the incorporation of transfer learning, attention mechanisms, and larger datasets, are suggested to enhance model robustness and clinical reliability. These findings demonstrate the potential of advanced deep learning models in automating brain tumor classification for clinical use.

Cardiovascular diseases (CVDs) have become increasingly prevalent and severe worldwide, necessitating a transition to more advanced and precise predictive models. Traditional approaches to the prognosis of coronary heart disease often lead to higher morbidity and mortality due to delayed early detection and inadequate individualized analysis. Machine learning (ML) techniques offer a groundbreaking solution, capable of analyzing vast amounts of patient data to provide early and accurate predictions of cardiac diseases. This shift holds great promise across various domains, including heart imaging, cardiothoracic surgery, cardiovascular anesthesiology, coronary critical care, pediatric cardiovascular disease, predictive cardiology, and interventional cardiology. In this comprehensive review, we evaluate several ML techniques, specifically focusing on classification and prediction algorithms, to assess their effectiveness in predicting cardiovascular diseases. We examine a range of algorithms such as K-Nearest Neighbors (K-NN), Decision Trees (DT), Support Vector Machines (SVM), Random Forests, Logistic Regression, XGBoost, Extra Trees Classifier, Gaussian Naive Bayes, and Artificial Neural Networks. By comparing these models, we aim to identify the most reliable and effective predictive tools for cardiovascular diseases. Notably, algorithms like Decision Trees and Extra Trees Classifiers have demonstrated remarkable performance, with recorded accuracies of 93.19% and 98.15%, respectively. This study underscores the importance of implementing machine learning approaches to enhance the accuracy and predictive capabilities in cardiac care. By leveraging these models, we can significantly improve early diagnosis, leading to better patient outcomes across cardiology specialties.