A comprehensive review for breast histopathology image analysis using classical and deep neural networks
Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …
histopathological images contain sufficient phenotypic information, they play an …
Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review
Breast cancer in women is the second most common cancer worldwide. Early detection of
breast cancer can reduce the risk of human life. Non-invasive techniques such as …
breast cancer can reduce the risk of human life. Non-invasive techniques such as …
Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning
MM Srikantamurthy, VPS Rallabandi, DB Dudekula… - BMC Medical …, 2023 - Springer
Background Grading of cancer histopathology slides requires more pathologists and expert
clinicians as well as it is time consuming to look manually into whole-slide images. Hence …
clinicians as well as it is time consuming to look manually into whole-slide images. Hence …
An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia
Automated and accurate diagnosis of Acute Lymphoblastic Leukemia (ALL), blood cancer, is
a challenging task. Nowadays, Convolutional Neural Networks (CNNs) have become a …
a challenging task. Nowadays, Convolutional Neural Networks (CNNs) have become a …
A generalized deep learning framework for whole-slide image segmentation and analysis
Histopathology tissue analysis is considered the gold standard in cancer diagnosis and
prognosis. Whole-slide imaging (WSI), ie, the scanning and digitization of entire histology …
prognosis. Whole-slide imaging (WSI), ie, the scanning and digitization of entire histology …
Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks
Manual delineation of tumours in breast histopathology images is generally time-consuming
and laborious. Computer-aided detection systems can assist pathologists by detecting …
and laborious. Computer-aided detection systems can assist pathologists by detecting …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
A review: The detection of cancer cells in histopathology based on machine vision
W He, T Liu, Y Han, W Ming, J Du, Y Liu, Y Yang… - Computers in Biology …, 2022 - Elsevier
Abstract Machine vision is being employed in defect detection, size measurement, pattern
recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection …
recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection …
Classification of hematoxylin and eosin‐stained breast cancer histology microscopy images using transfer learning with EfficientNets
C Munien, S Viriri - Computational Intelligence and …, 2021 - Wiley Online Library
Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The
process of diagnosis based on biopsy tissue is nontrivial, time‐consuming, and prone to …
process of diagnosis based on biopsy tissue is nontrivial, time‐consuming, and prone to …
Egdnet: an efficient glomerular detection network for multiple anomalous pathological feature in glomerulonephritis
Glomerulonephritis (GN) is a severe kidney disorder in which the tissues in the kidney
become inflamed and have problems filtering waste from the blood. Typical approaches for …
become inflamed and have problems filtering waste from the blood. Typical approaches for …