AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.

Visual analysis of peripheral blood smear slides using medical image analysis is required to diagnose red blood cell (RBC) morphological deformities caused by anemia. The absence of a complete anaemic RBC dataset has hindered the training and testing of deep convolutional neural networks (CNNs) for computer-aided analysis of RBC morphology. We introduce a benchmark RBC image dataset named Anemic RBC (AneRBC) to overcome this problem.

Blood cell image segmentation and classification: a systematic review.

Background: Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms.

Cervical cell's nucleus segmentation through an improved UNet architecture.

Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to its standout performance in medical image analysis, deep learning has gained attention from other techniques.

Automatic eczema classification in clinical images based on hybrid deep neural network.

The healthcare sector is the highest priority sector, and people demand the highest services and care. The fast rise of deep learning, particularly in clinical decision support tools, has provided exciting solutions primarily in medical imaging. In the past, ANNs (artificial neural networks) have been used extensively in dermatology and have shown promising results for detecting various skin diseases.