Related papers: ABCD: Automatic Blood Cell Detection via Attention…
Object detection and localization are crucial tasks for biomedical image analysis, particularly in the field of hematology where the detection and recognition of blood cells are essential for diagnosis and treatment decisions. While…
Blood cell detection in microscopic images is an essential branch of medical image processing research. Since disease detection based on manual checking of blood cells is time-consuming and full of errors, testing of blood cells using…
Blood cell detection is a typical small-scale object detection problem in computer vision. In this paper, we propose a CST-YOLO model for blood cell detection based on YOLOv7 architecture and enhance it with the CNN-Swin Transformer (CST),…
Complete blood cell detection holds significant value in clinical diagnostics. Conventional manual microscopy methods suffer from time inefficiency and diagnostic inaccuracies. Existing automated detection approaches remain constrained by…
Automated in-vitro cell detection and counting have been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab-on-chip…
Efficient detection and classification of blood cells are vital for accurate diagnosis and effective treatment of blood disorders. This study utilizes a YOLOv10 model trained on Roboflow data with images resized to 640x640 pixels across…
Object detection is of paramount importance in biomedical image analysis, particularly for lesion identification. While current methodologies are proficient in identifying and pinpointing lesions, they often lack the precision needed to…
This paper proposes a novel automatic classification framework for the recognition of five types of white blood cells. Segmenting complete white blood cells from blood smears images and extracting advantageous features from them remain…
This paper presents a comprehensive methodology and comparative performance analysis for the automated classification and object detection of peripheral blood cells (PBCs) in microscopic images. Addressing the critical challenge of data…
Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to…
Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions. In this study, we present a novel approach for classifying blood cell images known as…
Identification of abnormalities in red blood cells (RBC) is key to diagnosing a range of medical conditions from anaemia to liver disease. Currently this is done manually, a time-consuming and subjective process. This paper presents an…
Red blood cells are highly deformable and present in various shapes. In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis. However, manually labeling of all cells is laborious,…
Human vision exhibits remarkable adaptability in perceiving objects under camouflage. When color cues become unreliable, the visual system instinctively shifts its reliance from chrominance (color) to luminance (brightness and texture),…
While most previous automation-assisted reading methods can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extraction. This paper presents an efficient and totally…
Facial action unit (AU) detection remains a challenging task, due to the subtlety, dynamics, and diversity of AUs. Recently, the prevailing techniques of self-attention and causal inference have been introduced to AU detection. However,…
Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical…
Anatomical landmark detection in medical images is essential for various clinical and research applications, including disease diagnosis and surgical planning. However, manual landmark annotation is time-consuming and requires significant…
Machine learning has endless applications in the health care industry. White blood cell classification is one of the interesting and promising area of research. The classification of the white blood cells plays an important part in the…
Manual peripheral blood smear (PBS) analysis is labor intensive and subjective. While deep learning offers a promising alternative, a systematic evaluation of state of the art models such as YOLOv11 for fine grained PBS detection is still…