# Digital Imaging for Blood Diseases **Type:** Topics **Canonical URL:** https://scholariq.org/topics/digital-imaging-for-blood-diseases/ ## Facts | Field | Value | | --- | --- | | Description | This cluster of papers focuses on the automated analysis of blood cell images, particularly in the context of detecting malaria parasites and classifying leukemia. The research utilizes techniques such as image processing, convolutional neural networks, and machine learning for tasks including white blood cell segmentation, feature extraction, and automated diagnosis from microscopic blood images. | | Domain | Physical Sciences | | Field | Computer Science | | OpenAlex ID | t12874 | | Works | 151 | ## Topic papers all Showing 15 of 151. - [Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning](https://scholariq.org/papers/identifying-medical-diagnoses-and-treatable-diseases-by-image-based-deep/) - [Clinical-grade computational pathology using weakly supervised deep learning on whole slide images](https://scholariq.org/papers/clinical-grade-computational-pathology-using-weakly-supervised-deep-learning-on/) - [Weighted Res-UNet for High-Quality Retina Vessel Segmentation](https://scholariq.org/papers/weighted-res-unet-for-high-quality-retina-vessel-segmentation/) - [Detection of Coronavirus Disease (COVID-19) Based on Deep Features](https://scholariq.org/papers/detection-of-coronavirus-disease-covid-19-based-on-deep-features/) - [DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field](https://scholariq.org/papers/deepvessel-retinal-vessel-segmentation-via-deep-learning-and-conditional-random/) - [Glaucoma detection based on deep convolutional neural network](https://scholariq.org/papers/glaucoma-detection-based-on-deep-convolutional-neural-network/) - [ORIGA<sup>-light</sup>: An online retinal fundus image database for glaucoma analysis and research](https://scholariq.org/papers/origa-and-lt-sup-and-gt-light-and-lt-sup-and-gt-an-online-retinal-fundus-image/) - [Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases](https://scholariq.org/papers/accurate-leukocyte-detection-based-on-deformable-detr-and-multi-level-feature/) - [Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine](https://scholariq.org/papers/detection-of-coronavirus-disease-covid-19-based-on-deep-features-and-support/) - [IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge](https://scholariq.org/papers/idrid-diabetic-retinopathy-segmentation-and-grading-challenge/) - [Deep Learning–Based Histopathologic Assessment of Kidney Tissue](https://scholariq.org/papers/deep-learning-based-histopathologic-assessment-of-kidney-tissue/) - [Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks](https://scholariq.org/papers/automatic-detection-of-39-fundus-diseases-and-conditions-in-retinal-photographs/) - [Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture](https://scholariq.org/papers/microscopic-brain-tumor-detection-and-classification-using-scp-3d-cnn-scp-and/) - [Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review](https://scholariq.org/papers/algorithms-for-the-automated-detection-of-diabetic-retinopathy-using-digital/) - [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://scholariq.org/papers/rose-a-retinal-oct-angiography-vessel-segmentation-dataset-and-new-model/) ## Topic primary papers Showing 15 of 30. - [Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases](https://scholariq.org/papers/accurate-leukocyte-detection-based-on-deformable-detr-and-multi-level-feature/) - [Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application](https://scholariq.org/papers/deep-learning-based-automatic-malaria-parasite-detection-from-blood-smear-and/) - [An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia](https://scholariq.org/papers/an-efficient-deep-convolutional-neural-network-based-detection-and/) - [Fine-grained leukocyte classification with deep residual learning for microscopic images](https://scholariq.org/papers/fine-grained-leukocyte-classification-with-deep-residual-learning-for/) - [Differential box counting methods for estimating fractal dimension of gray-scale images: A survey](https://scholariq.org/papers/differential-box-counting-methods-for-estimating-fractal-dimension-of-gray-scale/) - [A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia](https://scholariq.org/papers/a-systematic-review-on-recent-advancements-in-deep-and-machine-learning-based/) - [White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks](https://scholariq.org/papers/white-blood-cell-differential-count-of-maturation-stages-in-bone-marrow-smear/) - [Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities](https://scholariq.org/papers/leukocytes-classification-and-segmentation-in-microscopic-blood-smear-a-resource/) - [Classification of white blood cells using weighted optimized deformable convolutional neural networks](https://scholariq.org/papers/classification-of-white-blood-cells-using-weighted-optimized-deformable/) - [An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders](https://scholariq.org/papers/an-efficient-blood-cell-segmentation-for-the-detection-of-hematological/) - [An Efficient Detection and Classification of Acute Leukemia Using Transfer Learning and Orthogonal Softmax Layer-Based Model](https://scholariq.org/papers/an-efficient-detection-and-classification-of-acute-leukemia-using-transfer/) - [A Review of Automated Methods for the Detection of Sickle Cell Disease](https://scholariq.org/papers/a-review-of-automated-methods-for-the-detection-of-sickle-cell-disease/) - [An Efficient VGG19 Framework for Malaria Detection in Blood Cell Images](https://scholariq.org/papers/an-efficient-vgg19-framework-for-malaria-detection-in-blood-cell-images/) - [A lightweight deep learning system for automatic detection of blood cancer](https://scholariq.org/papers/a-lightweight-deep-learning-system-for-automatic-detection-of-blood-cancer/) - [White Blood Cells Classification with Deep Convolutional Neural Networks](https://scholariq.org/papers/white-blood-cells-classification-with-deep-convolutional-neural-networks/) --- Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.