PMID- 38006210
OWN - NLM
STAT- MEDLINE
DCOM- 20240101
LR  - 20240106
IS  - 1476-8259 (Electronic)
IS  - 1025-5842 (Linking)
VI  - 27
IP  - 1
DP  - 2024 Jan-Mar
TI  - An ensemble-based serial cascaded attention network and improved variational auto 
      encoder for breast cancer prognosis prediction using data.
PG  - 98-115
LID - 10.1080/10255842.2023.2280883 [doi]
AB  - Breast cancer is one of the most common types of cancer in women and it produces 
      a huge amount of death rate in the world. Early recognition is lessening its 
      impact. The early recognition of breast cancer could convince patients to receive 
      surgical therapy, which will significantly improve the chance of restoration. 
      This information is used by the machine learning technique to find links between 
      them and appraise our forecasts of fresh occurrences. Later recognition of breast 
      cancer can lead to death. An accurate prescient framework for breast cancer 
      prediction is urgently needed in the current era. In order to accomplish the 
      objective, an adaptive ensemble model is proposed for breast cancer prognosis 
      prediction using data. At the initial stage, the raw data are fetched from 
      benchmark datasets. It is then followed by data cleaning and preprocessing. 
      Subsequently, the pre-processed data is fed into the Improved Variational 
      Autoencoder (IVAE), where the deep features are extracted. Finally, the resultant 
      features are given as input to the Ensemble-based Serial Cascaded Attention 
      Network (ESCANet), which is built with Deep Temporal Convolution Network (DTCN), 
      Bi-directional Long Short-Term Memory (BiLSTM), and Recurrent Neural Network 
      (RNN). The effectiveness of the model is validated and compared with conventional 
      methodologies. Therefore, the results elucidate that the proposed methodology 
      achieves extensive results; thus, it increases the system's efficiency.
FAU - Vanmathi, P
AU  - Vanmathi P
AD  - Full time Research Scholar, Department of ECE, KCG College of Technology, 
      Karapakkam, Chennai, Tamil Nadu, India.
FAU - Jose, Deepa
AU  - Jose D
AD  - Professor, Department of ECE, KCG College of Technology, Karapakkam, Chennai, 
      Tamil Nadu, India.
LA  - eng
PT  - Journal Article
DEP - 20231228
PL  - England
TA  - Comput Methods Biomech Biomed Engin
JT  - Computer methods in biomechanics and biomedical engineering
JID - 9802899
SB  - IM
MH  - Humans
MH  - Female
MH  - *Algorithms
MH  - *Breast Neoplasms/diagnosis
MH  - Neural Networks, Computer
MH  - Machine Learning
MH  - Prognosis
OTO - NOTNLM
OT  - Breast cancer prognosis prediction
OT  - Improved Variational Autoencoder
OT  - deep temporal convolution network
OT  - ensemble-based serial cascaded attention network
OT  - recurrent neural network
EDAT- 2023/11/25 12:46
MHDA- 2024/01/02 11:42
CRDT- 2023/11/25 03:40
PHST- 2024/01/02 11:42 [medline]
PHST- 2023/11/25 12:46 [pubmed]
PHST- 2023/11/25 03:40 [entrez]
AID - 10.1080/10255842.2023.2280883 [doi]
PST - ppublish
SO  - Comput Methods Biomech Biomed Engin. 2024 Jan-Mar;27(1):98-115. doi: 
      10.1080/10255842.2023.2280883. Epub 2023 Dec 28.