This study introduces a novel deep-learning methodology that is customized to automatically diagnose Alzheimer’s disease (AD) through the analysis of MRI datasets. The process of diagnosing AD via the visual examination of magnetic resonance imaging (MRI) presents considerable challenges. The visual diagnosis of mild to very mild stages of AD is challenging due to the MRI similarities observed between a brain that is aging normally and one that has AD. The detection of AD with extreme precision is critical during its early stages. Deep-learning techniques have recently been shown to be significantly more effective than human detection in identifying various stages of AD, enabling early-stage diagnosis. The aim of this research is to develop a deep-learning approach that utilizes pre-trained convolutional neural networks (CNNs) to accurately detect the severity levels of AD, particularly in situations where the quantity and quality of available datasets are limited. In this approach, the AD dataset is preprocessed via a refined image processing module prior to the training phase. The proposed method was compared to two well-known deep-learning algorithms (VGG16 and ResNet50) using four Kaggle AD datasets: one for the normal stage of the disease and three for the mild, very mild, and moderate stages, respectively. This allowed us to evaluate the effectiveness of the classification results. The three models were compared using six performance metrics. The results achieved with our approach indicate an overall detection accuracy of 99.3%, which is superior to the other existing models.

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

Due to their interesting size-dependent magnetic characteristics and relative biocompatibility, magnetic superparamagnetic iron oxide (SPIO) nanoparticles have been widely exploited as probes for cell and subcellular structure identification, as well as medication and gene delivery. A thorough understanding of the mechanics of the interaction between nanoparticles and macrophages is vital in managing dynamic processes in nanomedicine. In this study, the interaction behavior and uptake of SPIO nanoparticles by M1- and M2-type macrophages were investigated. Mice monocytes were differentiated into M1 and M2 macrophages, and the uptake of SPIO nanoparticles was studied using a TEM microscope. A high resolution image of 1 nm resolution, an image processing technique, was developed to extract the SPIO-NPs from tomographic TEM microscopic images. Lysosomes appear to be the zones of high concentrations of SPIO inside macrophages. Lysosomes were first selected in each image, and then segmentation by the Otsu thresholding method was used to extract the SPIO-NPs. The Otsu threshold method is a global thresholding technique used to automatically differentiate SPIOs from the background. The SPIO-NPs appear in red colors, and the other pixels in the image are considered background. Then, an estimation of the SPIO-NP uptakes by lysosomes is produced. Higher uptake of all-sized nanoparticles was observed in M1- and M2-type macrophages. An accurate estimation of the number of SPIO-NPs was obtained. This result will help in controlling targeted drug delivery and assessing the safety impact of the use of SPIO-NPs in nanomedicine for humans.

The purpose of this paper was to detect and separate the cluster intensity provided by Iron oxide nanoparticles (IO-NPs), in the MRI images, to investigate the drug delivery effectiveness. IO-NPs were attached to the macrophages and inserted into the eye of the inflamed mouse’s calf. The low resolution of MRI and the tiny dimension of the IO-NPs made the situation challenging. IO-NPs serve as a marker, due to their strong intensity in the MRI, enabling us to follow the track of the macrophages. An image processing procedure was developed to estimate the position and the amount of IO-NPs spreading inside the inflamed mouse leg. A fuzzy Clustering algorithm was adopted to select the region of interest (ROI). A 3D model of the femoral region was used for the detection and then the extraction IO-NPs in the MRI images. The results achieved prove the effectiveness of the proposed method to improve the control process of targeted drug delivered. It helps in optimizing the treatment and opens a promising novel research axis for nanomedicine applications.

The automatic detection of objects in medical photographs is an essential component of the diagnostic procedure. The issue of early-stage brain tumor detection has progressed significantly with the use of deep learning algorithms (DLA), particularly convolutional neural networks (CNN). The issue lies in the fact that these algorithms necessitate a training phase involving a large database over several hundred images, which can be time-consuming and require complex computational infrastructure. This study aimed to comprehensively evaluate a proposed method, which relies on an active contour algorithm, for identifying and distinguishing brain tumors in magnetic resonance images. We tested the proposed algorithm using 50 brain images, specifically focusing on glioma tumors, while 2000 images were used for DLA from the BRATS Challenges 2021. The proposed segmentation method is made up of an active contour algorithm, an anisotropic diffusion filter for pre-processing, active contour segmentation (Chan-Vese), and morphologic operations for segmentation refinement. We evaluated its performance using various metrics, such as accuracy, precision, sensitivity, specificity, Jaccard index, Dice index, and Hausdorff distance. The proposed method provided an average of the first six performance metrics of 0.96, which is higher than most classical image segmentation methods and was comparable to the deep learning methods, which have an average performance score of 0.98. These results indicate its ability to detect brain tumors accurately and rapidly. The results section provided both numerical and visual insights into the similarity between segmented and ground truth tumor areas. The findings of this study highlighted the potential of computer-based methods in improving brain tumor identification using magnetic resonance imaging. Future work must validate the efficacy of these segmentation approaches across different brain tumor categories and improve computing efficiency to integrate the technology into clinical processes.