A systematic literature review of breast cancer diagnosis using machine intelligence techniques
Breast cancer is one of the most common diseases in women; it can have long-term
implications and can even be fatal. However, early detection, achieved through recent …
implications and can even be fatal. However, early detection, achieved through recent …
An extensive review of state-of-the-art transfer learning techniques used in medical imaging: Open issues and challenges
AA Mukhlif, B Al-Khateeb… - Journal of Intelligent …, 2022 - degruyter.com
Deep learning techniques, which use a massive technology known as convolutional neural
networks, have shown excellent results in a variety of areas, including image processing …
networks, have shown excellent results in a variety of areas, including image processing …
MetaMed: Few-shot medical image classification using gradient-based meta-learning
The occurrence of long-tailed distributions and unavailability of high-quality annotated
images is a common phenomenon in medical datasets. The use of conventional Deep …
images is a common phenomenon in medical datasets. The use of conventional Deep …
A novel chaos-based privacy-preserving deep learning model for cancer diagnosis
Early cancer identification is regarded as a challenging problem in cancer prevention for the
healthcare community. In addition, ensuring privacy-preserving healthcare data becomes …
healthcare community. In addition, ensuring privacy-preserving healthcare data becomes …
A transfer learning approach to breast cancer classification in a federated learning framework
Artificial intelligence (AI) technologies have seen strong development. Many applications
now use AI to diagnose breast cancer. However, most new research has only been …
now use AI to diagnose breast cancer. However, most new research has only been …
Hyperparameter optimizer with deep learning-based decision-support systems for histopathological breast cancer diagnosis
Simple Summary This study develops an arithmetic optimization algorithm with deep-
learning-based histopathological breast cancer classification (AOADL-HBCC) technique for …
learning-based histopathological breast cancer classification (AOADL-HBCC) technique for …
Deep learning for the classification of small-cell and non-small-cell lung cancer
M Kriegsmann, C Haag, CA Weis, G Steinbuss… - Cancers, 2020 - mdpi.com
Reliable entity subtyping is paramount for therapy stratification in lung cancer.
Morphological evaluation remains the basis for entity subtyping and directs the application …
Morphological evaluation remains the basis for entity subtyping and directs the application …
Pfemed: Few-shot medical image classification using prior guided feature enhancement
Deep learning-based methods have recently demonstrated outstanding performance on
general image classification tasks. As optimization of these methods is dependent on a large …
general image classification tasks. As optimization of these methods is dependent on a large …
FABNet: fusion attention block and transfer learning for laryngeal cancer tumor grading in P63 IHC histopathology images
Laryngeal cancer tumor (LCT) grading is a challenging task in P63 Immunohistochemical
(IHC) histopathology images due to small differences between LCT levels in pathology …
(IHC) histopathology images due to small differences between LCT levels in pathology …
Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM
R Sharma, S Shrivastava, S Kumar Singh… - Briefings in …, 2022 - academic.oup.com
Fungal infections or mycosis cause a wide range of diseases in humans and animals. The
incidences of community acquired; nosocomial fungal infections have increased …
incidences of community acquired; nosocomial fungal infections have increased …