[HTML][HTML] A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images
A Labrada, BD Barkana - Bioengineering, 2023 - mdpi.com
Breast cancer is the second most common cancer in women who are mainly middle-aged
and older. The American Cancer Society reported that the average risk of developing breast …
and older. The American Cancer Society reported that the average risk of developing breast …
Breast cancer diagnosis from histopathology images using deep neural network and XGBoost
Abstract Background and Objective: Globally, breast cancer is one of the most common
diseases among women. As a result of the disadvantages of manual analysis, computer …
diseases among women. As a result of the disadvantages of manual analysis, computer …
[HTML][HTML] Enhanced pre-trained xception model transfer learned for breast cancer detection
Early detection and timely breast cancer treatment improve survival rates and patients'
quality of life. Hence, many computer-assisted techniques based on artificial intelligence are …
quality of life. Hence, many computer-assisted techniques based on artificial intelligence are …
[HTML][HTML] Fine tuning deep learning models for breast tumor classification
This paper proposes an approach to enhance the differentiation task between benign and
malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The …
malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The …
Discriminative Dictionary Learning Using Penalized Rank-1 Approximation for Breast Cancer Classification With Imbalanced Dataset
In histopathological image analysis, the feature extraction task for classification proves to be
demanding. This difficulty arises from the assortment of histological features appropriate for …
demanding. This difficulty arises from the assortment of histological features appropriate for …
[PDF][PDF] Texture Features of Grey Level Size Zone Matrix for Breast Cancer Detection
RA Abtan, AH Al-Saleh, HJ Mohamed, HK Abbas… - Iraqi Journal of …, 2023 - iasj.net
The texture analysis of cancer cells leads to a procedure to distinguish spatial differences
within an image and extract essential information. This study used two test tumours images …
within an image and extract essential information. This study used two test tumours images …
A transformer model guided by histopathological image information for DCE-MRI-based prediction of response to neoadjuvant chemotherapy in breast cancer
Z Yu, M Fan, Y Chen, X Xiao, X Pan… - Medical Imaging 2024 …, 2024 - spiedigitallibrary.org
Pathologic diagnosis is the" gold standard" for diagnosing breast cancer and is increasingly
used to assess the response to Neoadjuvant Chemotherapy (NACT). Despite its high …
used to assess the response to Neoadjuvant Chemotherapy (NACT). Despite its high …
ConTenNet: Quantum Tensor-augmented Convolutional Representations for Breast Cancer Histopathological Image Classification
J Liu, H Lai, J Ma, S Pang - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
In recent years, deep convolutional neural networks (CNNs) have been spectacularly
successful in the classification and diagnosis of breast cancer and its histopathological …
successful in the classification and diagnosis of breast cancer and its histopathological …
Explainable deep transferlearning approach for classifying oral tongue lesions
K Ranasinghe - 2024 - oulurepo.oulu.fi
Oral cancer has recently become a prevalent disease worldwide, with the highest reported
cases in South Asian region. Due to the heterogeneous features of oral tongue cancers …
cases in South Asian region. Due to the heterogeneous features of oral tongue cancers …