Abstract: Glaucoma, a leading cause of irreversible blindness, requires precise segmentation of the optic disc and optic cup in fundus images for early diagnosis and progression monitoring. This study ...
Abstract: Breast cancer is a highly complex disease that requires precise molecular subtyping to guide tailored treatment strategies. In this study, we employed a marker-based watershed segmentation ...
Abstract: The field of document processing has made remarkable strides with the integration of computer vision and machine learning. This progress extends to tasks like text extraction from essential ...
Abstract: The shape and size of the placenta are closely related to fetal development in the second and third trimesters of pregnancy. Accurately segmenting the placental contour in ultrasound images ...
Abstract: Vitamin deficiency is a widespread global health issue that affects millions, often leading to severe physiological and dermatological complications. Early detection is essential for timely ...
Abstract: Face authentication (FA) schemes are universally adopted. However, current FA systems are mainly camera-based and susceptible to masks and vulnerable to spoofing attacks. This paper exploits ...
Abstract: Reconfigurable intelligent surfaces (RISs) are an emerging technology for improving spectral efficiency and reducing power consumption in future wireless systems. This paper investigates the ...
Abstract: Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based ...
Abstract: Electroencephalography (EEG) is an effective assessment tool to identify autism spectrum disorders with low cost, and deep learning has been applied in EEG analysis for extracting meaningful ...
Abstract: Contrastive Language-Image Pre-training (CLIP) learns robust visual models through language supervision, making it a crucial visual encoding technique for various applications. However, CLIP ...
Abstract: Convolutional neural networks (CNNs) have been foundational in deep learning architectures for image processing, and recently, Transformer networks have emerged, bringing further ...
Abstract: This research suggests a strong framework for automated malaria detection using a Convolutional Neural Network (CNN) model. The dataset, sourced from Kaggle, consists of 27,558 ...