Deep learning based methods for breast cancer diagnosis: a systematic review and future direction
Breast cancer is one of the precarious conditions that affect women, and a substantive cure
has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep …
has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep …
A review on recent developments in cancer detection using Machine Learning and Deep Learning models
Cancer is a fatal illness frequently caused by a variety of obsessive changes and genetic
disorders. Cancer cells knowing as abnormal cells can grow in any part of the human body …
disorders. Cancer cells knowing as abnormal cells can grow in any part of the human body …
Vision-transformer-based transfer learning for mammogram classification
Breast mass identification is a crucial procedure during mammogram-based early breast
cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or …
cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or …
BF2SkNet: best deep learning features fusion-assisted framework for multiclass skin lesion classification
The convolutional neural network showed considerable success in medical imaging with
explainable AI for cancer detection and recognition. However, the irrelevant and large …
explainable AI for cancer detection and recognition. However, the irrelevant and large …
BUVITNET: Breast ultrasound detection via vision transformers
Convolutional neural networks (CNNs) have enhanced ultrasound image-based early
breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the …
breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the …
Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer
Breast cancer (BC) is one of the most common types of cancer in women, and its prevalence
is on the rise. The diagnosis of this disease in the first steps can be highly challenging …
is on the rise. The diagnosis of this disease in the first steps can be highly challenging …
A federated learning framework for breast cancer histopathological image classification
L Li, N **e, S Yuan - Electronics, 2022 - mdpi.com
Quantities and diversities of datasets are vital to model training in a variety of medical image
diagnosis applications. However, there are the following problems in real scenes: the …
diagnosis applications. However, there are the following problems in real scenes: the …
B2C3NetF2: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature …
Currently, the improvement in AI is mainly related to deep learning techniques that are
employed for the classification, identification, and quantification of patterns in clinical …
employed for the classification, identification, and quantification of patterns in clinical …
Two-stage deep learning method for breast cancer detection using high-resolution mammogram images
B Ibrokhimov, JY Kang - Applied Sciences, 2022 - mdpi.com
Breast cancer screening and detection using high-resolution mammographic images have
always been a difficult task in computer vision due to the presence of very small yet clinically …
always been a difficult task in computer vision due to the presence of very small yet clinically …
[HTML][HTML] Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequences
Breast cancer is the most prevalent disease that poses a significant threat to women's
health. Despite the Dynamic Contrast-Enhanced MRI (DCE-MRI) has been widely used for …
health. Despite the Dynamic Contrast-Enhanced MRI (DCE-MRI) has been widely used for …