DMIP

Damaged road surfaces can cause traffic accidents and negatively affect the tires and suspension of vehicles, potentially contributing to accidents. Rapid detection is needed to prevent this. To overcome the limitations of human inspection of hundreds of ...

In this paper, we focus on 14 types of bacteria, that is, 4 types of Gram positive cocci (Enterococcus faecalis, Staphylococcus aureus, Streptococcus pneumoniae and Group B Streptococcus), 1 type of Gram negative cocci (Branhamella catarrhalis), 3 types ...

Face detection accuracy for mask wearers is generally lower than that for non-mask wearers. Furthermore, in situations when the camera performance is low or the shooting environment is poor, blurring may occur, and further reducing detection accuracy. In ...

In this paper, we detect phagocytotic activity of leukocytes from Gram stained smear images by using object detectors YOLOv5 and YOLOv8. Then, we detect three kinds of regions representing phagocytotic, quasi-phagocytotic and non-phagocytotic activities ...

Landmark detection is an essential foundation of cephalometric analysis, playing a crucial role in clinical diagnosis and treatment of orthodontic and orthognathic conditions. This paper proposes a novel framework that enables targeted learning and ...

In recent years, human face recognition technology has made remarkable progress. However, research on animal face recognition has not progressed as much as that on human face recognition. In this study, we focus on cats. The detection of facial landmarks ...

Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddressed or insufficiently addressed ...

This research investigation explores the transformative potential of deep learning, particularly Convolutional Neural Networks (CNN) with U-Net architectures, in revolutionizing medical diagnostics through image detection and recognition. Medical imaging ...

Diabetic retinopathy (DR) is a severe eye disease that affects people with diabetes. In its early stages, DR often presents no symptoms, but if it is not treated, it can lead to blindness. Early diagnosis is critical to preventing this progression. This ...

Non-Cartesian k-space MRI trajectories are faster and more stable in motion than Cartesian trajectories. However, MRI reconstruction is limited by their sampling speed and various artifacts, resulting in slow and prone to blur problems. To enhance MRI ...

Image restoration is a field that has been studied for a long time, especially blurred image restoration is difficult to restore and is still being studied. Therefore, this study aims to improve the accuracy of learning-based blurred image ...

Emotion recognition is expected to play a critical role in improving user experiences for digital products in the near future. In this context, most of the past work has emphasized on emotion recognition through images or video streams, whereas only ...

Efficient image processing architectures are consistently in demand across a multitude of applications, particularly those customized for resource-constrained systems-on-chip (SoC). The increasing need for high-performance image processing in various ...

Image reconstruction is critical in medical imaging, where accurate data restoration is essential for precise analysis and diagnosis. This research proposes an innovative architectural framework for medical image reconstruction, termed the Dual Encoder ...

In this paper, we predict yeast-like fungi from Gram stained smears images. First, we confirm that we can classify yeast-like fungi from Clostridium perfringens and Corynebacterium as Gram positive bacilli completely by using the image classifiers of ...

Endoscopic third ventriculostomy (ETV) is a minimally invasive and a viable neuroendoscopic procedure in the treatment of obstructive hydrocephalus. However, a range of complications have been reported during ETV in connection with the surgical procedure ...

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver ...

Deep learning has transformed medical imaging by significantly improving accuracy and efficiency in image processing tasks such as disease detection, segmentation, and classification. This paper explores the role of convolutional neural networks (CNNs), ...

As computer vision and image analysis technologies rapidly mature, they can revolutionize medical imaging, ushering in a new era of precision in diagnosis and treatment. In this research study, we explore innovative methodologies that leverage these ...

The demand for high-performance hardware solutions for machine learning tasks is growing as medical imaging evolves. In this paper, we will focus on the latest hardware advanced technologies: GPUs, TPUs and FPGAs that can be used for image processing ...

Concept of the Study: The human body is an intricate matrix, and solving health problems through modern medicine requires us to lay out the insides of this matrix in full force from within. Computational Anatomy (CA) is a rapidly growing field that uses ...