[HTML][HTML] Classification framework for medical diagnosis of brain tumor with an effective hybrid transfer learning model
Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the
world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require …
world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require …
[HTML][HTML] A hybrid workflow of residual convolutional transformer encoder for breast cancer classification using digital X-ray mammograms
Breast cancer, which attacks the glandular epithelium of the breast, is the second most
common kind of cancer in women after lung cancer, and it affects a significant number of …
common kind of cancer in women after lung cancer, and it affects a significant number of …
[HTML][HTML] BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification
Blood cells carry important information that can be used to represent a person's current state
of health. The identification of different types of blood cells in a timely and precise manner is …
of health. The identification of different types of blood cells in a timely and precise manner is …
[HTML][HTML] A new univariate feature selection algorithm based on the best–worst multi-attribute decision-making method
DPM Abellana, DM Lao - Decision Analytics Journal, 2023 - Elsevier
With the extensive applicability of machine learning classification algorithms to a wide
spectrum of domains, feature selection (FS) becomes a relevant data preprocessing …
spectrum of domains, feature selection (FS) becomes a relevant data preprocessing …
[HTML][HTML] ETECADx: Ensemble self-attention transformer encoder for breast cancer diagnosis using full-field digital X-ray breast images
Early detection of breast cancer is an essential procedure to reduce the mortality rate among
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
[HTML][HTML] Breast cancer classification through meta-learning ensemble technique using convolution neural networks
This study aims to develop an efficient and accurate breast cancer classification model using
meta-learning approaches and multiple convolutional neural networks. This Breast …
meta-learning approaches and multiple convolutional neural networks. This Breast …
[HTML][HTML] RNN and BiLSTM fusion for accurate automatic epileptic seizure diagnosis using EEG signals
Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to
seizures alone. They comprise a wide spectrum of problems that might impair and reduce …
seizures alone. They comprise a wide spectrum of problems that might impair and reduce …
[HTML][HTML] Detection and classification of histopathological breast images using a fusion of CNN frameworks
A Rafiq, A Chursin, W Awad Alrefaei… - Diagnostics, 2023 - mdpi.com
Breast cancer is responsible for the deaths of thousands of women each year. The diagnosis
of breast cancer (BC) frequently makes the use of several imaging techniques. On the other …
of breast cancer (BC) frequently makes the use of several imaging techniques. On the other …
[PDF][PDF] SNSVM: SqueezeNet-guided SVM for breast cancer diagnosis
Breast cancer is a major public health concern that affects women worldwide. It is a leading
cause of cancer-related deaths among women, and early detection is crucial for successful …
cause of cancer-related deaths among women, and early detection is crucial for successful …
[HTML][HTML] Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted
Breast cancer is the second most common type of cancer among women, and it can threaten
women's lives if it is not diagnosed early. There are many methods for detecting breast …
women's lives if it is not diagnosed early. There are many methods for detecting breast …