TY - JOUR
T1 - DPV
T2 - A taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection
AU - Shah, Bhagyashree
AU - Alsadoon, Abeer
AU - Prasad, P. W.C.
AU - Al-Naymat, Ghazi
AU - Beg, Azam
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2021/6
Y1 - 2021/6
N2 - Deep learning (DL) is a type of machine learning capable of processing large quantities of data to provide analytic results based on a particular framework’s parameters and aims. DL is widely used in a variety of fields, including medicine. Currently, there are various DL-based prediction models for predicting cancer probability and survival. However, the specific problem is that no integrated system can predict cancer survival, probability, and presence in the medical patient’s samples. Therefore, this research investigates the latest literature in the field of DL-based cancer prediction models for predicting the cancer probability and the patient survival rate. The name of this proposed model is Multimodal Incremental Recurrent Deep Neural Network; it can perform the analysis, prediction, and diagnosis of cancer using multi-dimensional data processing. It can also predict the cancer possibility and survival using incremental recurrent neural networks. The components of the proposed taxonomy are Data, Prediction technique, and View (DPV). This research’s contribution is the critical analysis of the latest literature on the DL-based systems that can predict cancer and its outcomes. It provides a theoretical model that can predict the possibility, presence, and survival of cancer by processing multi-dimensional medical samples of the patient to make accurate predictions. We also highlight the importance of the proposed taxonomy.
AB - Deep learning (DL) is a type of machine learning capable of processing large quantities of data to provide analytic results based on a particular framework’s parameters and aims. DL is widely used in a variety of fields, including medicine. Currently, there are various DL-based prediction models for predicting cancer probability and survival. However, the specific problem is that no integrated system can predict cancer survival, probability, and presence in the medical patient’s samples. Therefore, this research investigates the latest literature in the field of DL-based cancer prediction models for predicting the cancer probability and the patient survival rate. The name of this proposed model is Multimodal Incremental Recurrent Deep Neural Network; it can perform the analysis, prediction, and diagnosis of cancer using multi-dimensional data processing. It can also predict the cancer possibility and survival using incremental recurrent neural networks. The components of the proposed taxonomy are Data, Prediction technique, and View (DPV). This research’s contribution is the critical analysis of the latest literature on the DL-based systems that can predict cancer and its outcomes. It provides a theoretical model that can predict the possibility, presence, and survival of cancer by processing multi-dimensional medical samples of the patient to make accurate predictions. We also highlight the importance of the proposed taxonomy.
KW - Artificial intelligence
KW - Cancer prediction
KW - Deep learning
KW - Machine learning
KW - Survival
UR - http://www.scopus.com/inward/record.url?scp=85102950621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102950621&partnerID=8YFLogxK
U2 - 10.1007/s11042-021-10769-4
DO - 10.1007/s11042-021-10769-4
M3 - Article
AN - SCOPUS:85102950621
SN - 1380-7501
VL - 80
SP - 21339
EP - 21361
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 14
ER -