Automated analysis of blood smear images for leukemia detection: a comprehensive review

A Mittal, S Dhalla, S Gupta, A Gupta - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
Leukemia, the malignancy of blood-forming tissues, becomes fatal if not detected in the early
stages. It is detected through a blood smear test that involves the morphological analysis of …

Automated screening system for acute myelogenous leukemia detection in blood microscopic images

S Agaian, M Madhukar… - IEEE Systems …, 2014 - ieeexplore.ieee.org
Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is prevalent
among adults. The average age of a person with AML is 65 years. The need for automation …

Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier

D Goutam, S Sailaja - 2015 IEEE International Conference on …, 2015 - ieeexplore.ieee.org
Blood cancer is a form of cancer which attacks the blood, bone marrow, or lymphatic system.
It is diagnosed with a blood test in which specific types of blood cells are counted by …

The quantitative criteria based on the fractal dimensions, entropy, and lacunarity for the spatial distribution of cancer cell nuclei enable identification of low or high …

P Waliszewski - Frontiers in physiology, 2016 - frontiersin.org
Background: Tumor grading, PSA concentration, and stage determine a risk of prostate
cancer patients with accuracy of about 70%. An approach based on the fractal geometrical …

Quaternion neural networks applied to prostate cancer gleason grading

A Greenblatt, C Mosquera-Lopez… - 2013 IEEE international …, 2013 - ieeexplore.ieee.org
Diagnosis of prostate cancer currently involves visual examination of samples for the
assignment of Gleason grades using a microscope, a time-consuming and subjective …

Automated mitosis detection in histopathology based on non-gaussian modeling of complex wavelet coefficients

T Wan, W Zhang, M Zhu, J Chen, A Achim, Z Qin - Neurocomputing, 2017 - Elsevier
To diagnose breast cancer, the number of mitotic cells present in histology sections is an
important indicator for examining and grading biopsy specimen. This study aims at …

[HTML][HTML] Detecting and segmenting cell nuclei in two-dimensional microscopy images

C Liu, F Shang, JA Ozolek, GK Rohde - Journal of Pathology Informatics, 2016 - Elsevier
Introduction: Cell nuclei are important indicators of cellular processes and diseases.
Segmentation is an essential stage in systems for quantitative analysis of nuclei extracted …

Analysis–synthesis learning with shared features: Algorithms for histology image classification

X Li, V Monga, UKA Rao - IEEE Transactions on Biomedical …, 2019 - ieeexplore.ieee.org
Objective: The diversity of tissue structure in histopathological images makes feature
extraction for classification a challenging task. Dictionary learning within a sparse …

[HTML][HTML] An Analytical Study on the Utility of RGB and Multispectral Imagery with Band Selection for Automated Tumor Grading

S Kunhoth, S Al-Maadeed - Diagnostics, 2024 - mdpi.com
The implementation of tumor grading tasks with image processing and machine learning
techniques has progressed immensely over the past several years. Multispectral imaging …

A hierarchical classifier for multiclass prostate histopathology image gleason grading

D Albashish, S Sahran, A Abdullah… - … of Information and …, 2018 - e-journal.uum.edu.my
Automated classification of prostate histopathology images includes the identification of
multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass …