Cervical cancer is the second most common cancer type in women. This is a deadly disease
that could benefit from early detection methods. Cytology is a possible, noninvasive …

Automatic analysis of histopathology specimens images can be utilized in early extraction
and detection of diseases such brain tumor, breast malignancy, colon cancer etc. The early …

The nuclei segmentation of microscopic images is a key pre-requisite for cancerous
pathological image analysis. However, an accurate nuclei cell segmentation is a long …

Nuclei segmentation is an initial step in the automated analysis of digitized microscopic
images. This paper focuses on utilizing the LinkNET-34 architecture for semantic …

Pathological diagnosis is considered to be declarative and authoritative. However, reading
pathology slides is a challenging task. Different parts of the section are taken and read for …

Histopathology image segmentation is a significant step in the early detection of diseases.
Compared to traditional segmentation methods, deep learning models provide better …

The analysis of hematoxylin and eosin (H&E) stained images obtained from breast tissue
biopsies is one of the most dependable ways to obtain an accurate diagnosis for breast …

As is known, pathologists visually examine the tissue distributions by using microscopes
traditionally. The rise in digital image processing and machine learning also allows high …

Automated nuclei segmentation techniques in histopathological image analysis continue to
improve. The machine learning model requires the annotation of large data sets which is a …

Identifying cells nuclei is the starting point for most biomedical analyzes, because cells
contain a nucleus filled with DNA, automating the detection of cells nuclei will speed up …