A deep convolutional neural network for classification of red blood cells in sickle cell anemia
Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion
accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients …
accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients …
[HTML][HTML] Mining textural knowledge in biological images: Applications, methods and trends
Texture analysis is a major task in many areas of computer vision and pattern recognition,
including biological imaging. Indeed, visual textures can be exploited to distinguish specific …
including biological imaging. Indeed, visual textures can be exploited to distinguish specific …
Deep learning based HEp-2 image classification: A comprehensive review
Classification of HEp-2 cell patterns plays a significant role in the indirect
immunofluorescence test for identifying autoimmune diseases in the human body. Many …
immunofluorescence test for identifying autoimmune diseases in the human body. Many …
HEp-2 cell image classification with deep convolutional neural networks
Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many
autoimmune diseases. This paper proposes an automatic framework for this classification …
autoimmune diseases. This paper proposes an automatic framework for this classification …
Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection
Different types of breast cancer are affecting lives of women across the world. Common
types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular …
types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular …
A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning
Abstract Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important
role in the diagnosis of many autoimmune diseases and subsequent treatment. One of the …
role in the diagnosis of many autoimmune diseases and subsequent treatment. One of the …
A novel fused convolutional neural network for biomedical image classification
With the advent of biomedical imaging technology, the number of captured and stored
biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and …
biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and …
Transfer learning of a convolutional neural network for HEp-2 cell image classification
The recognition of the staining patterns of Human Epithelial-2 (HEp-2) cells in indirect
immunofluorescence (IIF) images is essential for the diagnosis of several autoimmune …
immunofluorescence (IIF) images is essential for the diagnosis of several autoimmune …
[HTML][HTML] An automated pattern recognition system for classifying indirect immunofluorescence images of HEp-2 cells and specimens
Immunofluorescence antinuclear antibody tests are important for diagnosis and
management of autoimmune conditions; a key step that would benefit from reliable …
management of autoimmune conditions; a key step that would benefit from reliable …
Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images
Initiation, progression, and therapeutic response in cancer are largely influenced by tumor
microenvironment. Segmentation of tumor into epithelial vs. stromal regions constitutes the …
microenvironment. Segmentation of tumor into epithelial vs. stromal regions constitutes the …