BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights
There are several breast cancer datasets for building Computer Aided Diagnosis systems
(CADs) using either deep learning or traditional models. However, most of these datasets …
(CADs) using either deep learning or traditional models. However, most of these datasets …
Deep learning applied for histological diagnosis of breast cancer
Deep learning, as one of the currently most popular computer science research trends,
improves neural networks, which has more and deeper layers allowing higher abstraction …
improves neural networks, which has more and deeper layers allowing higher abstraction …
Classification of breast cancer histopathological images using DenseNet and transfer learning
Breast cancer is one of the most common invading cancers in women. Analyzing breast
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
How advanced technological approaches are reshaping sustainable social media crisis management and communication: a systematic review
The end goal of technological advancement used in crisis response and recovery is to
prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery …
prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery …
Federated fusion of magnified histopathological images for breast tumor classification in the internet of medical things
Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be
automated with the potential of Artificial Intelligence (AI). However, challenges arise when …
automated with the potential of Artificial Intelligence (AI). However, challenges arise when …
Going deeper: magnification‐invariant approach for breast cancer classification using histopathological images
Breast cancer has the highest fatality for women compared with other types of cancer.
Generally, early diagnosis of cancer is crucial to increase the chances of successful …
Generally, early diagnosis of cancer is crucial to increase the chances of successful …
Cnn based autoencoder application in breast cancer image retrieval
AE Minarno, KM Ghufron, TS Sabrila… - … Technology and Its …, 2021 - ieeexplore.ieee.org
Content Based Medical Image Retrieval (CBMIR) is considered as a common technique to
retrieve relevant images by comparing the features contained in the query image with the …
retrieve relevant images by comparing the features contained in the query image with the …
Deep neural network incorporating domain and resolution transformations model for histopathological image classification
To accurately diagnose breast cancer, pathologists take biopsy and perform microscopic
examination. However, this procedure is inconvenient, time-consuming, and requires high …
examination. However, this procedure is inconvenient, time-consuming, and requires high …
Accuracy improvement in binary and multi-class classification of breast histopathology images
Breast cancer is widely common in women. In order to recognize this type of cancer,
experienced pathologists must evaluate cell shapes in breast histopathology images in …
experienced pathologists must evaluate cell shapes in breast histopathology images in …
Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging
Z Tabatabaei, A Colomer, JAO Moll… - arXiv preprint arXiv …, 2024 - arxiv.org
Computer Aid Diagnosis (CAD) has developed digital pathology with Deep Learning (DL)-
based tools to assist pathologists in decision-making. Content-Based Histopathological …
based tools to assist pathologists in decision-making. Content-Based Histopathological …