PMID- 36611361
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230111
IS  - 2075-4418 (Print)
IS  - 2075-4418 (Electronic)
IS  - 2075-4418 (Linking)
VI  - 13
IP  - 1
DP  - 2022 Dec 26
TI  - Correlated-Weighted Statistically Modeled Contourlet and Curvelet Coefficient 
      Image-Based Breast Tumor Classification Using Deep Learning.
LID - 10.3390/diagnostics13010069 [doi]
LID - 69
AB  - Deep learning-based automatic classification of breast tumors using parametric 
      imaging techniques from ultrasound (US) B-mode images is still an exciting 
      research area. The Rician inverse Gaussian (RiIG) distribution is currently 
      emerging as an appropriate example of statistical modeling. This study presents a 
      new approach of correlated-weighted contourlet-transformed RiIG (CWCtr-RiIG) and 
      curvelet-transformed RiIG (CWCrv-RiIG) image-based deep convolutional neural 
      network (CNN) architecture for breast tumor classification from B-mode ultrasound 
      images. A comparative study with other statistical models, such as Nakagami and 
      normal inverse Gaussian (NIG) distributions, is also experienced here. The 
      weighted entitled here is for weighting the contourlet and curvelet sub-band 
      coefficient images by correlation with their corresponding RiIG statistically 
      modeled images. By taking into account three freely accessible datasets 
      (Mendeley, UDIAT, and BUSI), it is demonstrated that the proposed approach can 
      provide more than 98 percent accuracy, sensitivity, specificity, NPV, and PPV 
      values using the CWCtr-RiIG images. On the same datasets, the suggested method 
      offers superior classification performance to several other existing strategies.
FAU - Kabir, Shahriar M
AU  - Kabir SM
AUID- ORCID: 0000-0001-7399-7436
AD  - Department of Electrical and Electronic Engineering, Green University of 
      Bangladesh, Dhaka 1207, Bangladesh.
AD  - Department of Electrical and Electronic Engineering, Bangladesh University of 
      Engineering and Technology, Dhaka 1000, Bangladesh.
FAU - Bhuiyan, Mohammed I H
AU  - Bhuiyan MIH
AD  - Department of Electrical and Electronic Engineering, Bangladesh University of 
      Engineering and Technology, Dhaka 1000, Bangladesh.
LA  - eng
PT  - Journal Article
DEP - 20221226
PL  - Switzerland
TA  - Diagnostics (Basel)
JT  - Diagnostics (Basel, Switzerland)
JID - 101658402
PMC - PMC9818942
OTO - NOTNLM
OT  - B-mode ultrasound
OT  - Rician inverse Gaussian
OT  - breast cancer
OT  - contourlet
OT  - convolutional neural network (CNN)
OT  - curvelet
OT  - deep learning
OT  - machine learning
OT  - parametric image
COIS- The authors declare no conflicts of interest.
EDAT- 2023/01/09 06:00
MHDA- 2023/01/09 06:01
PMCR- 2022/12/26
CRDT- 2023/01/08 01:02
PHST- 2022/10/17 00:00 [received]
PHST- 2022/12/14 00:00 [revised]
PHST- 2022/12/22 00:00 [accepted]
PHST- 2023/01/08 01:02 [entrez]
PHST- 2023/01/09 06:00 [pubmed]
PHST- 2023/01/09 06:01 [medline]
PHST- 2022/12/26 00:00 [pmc-release]
AID - diagnostics13010069 [pii]
AID - diagnostics-13-00069 [pii]
AID - 10.3390/diagnostics13010069 [doi]
PST - epublish
SO  - Diagnostics (Basel). 2022 Dec 26;13(1):69. doi: 10.3390/diagnostics13010069.