PMID- 36373132
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230214
IS  - 0941-0643 (Print)
IS  - 1433-3058 (Electronic)
IS  - 0941-0643 (Linking)
VI  - 35
IP  - 7
DP  - 2023
TI  - Deep feature selection using local search embedded social ski-driver optimization 
      algorithm for breast cancer detection in mammograms.
PG  - 5479-5499
LID - 10.1007/s00521-022-07895-x [doi]
AB  - Breast cancer has become a common malignancy in women. However, early detection 
      and identification of this disease can save many lives. As computer-aided 
      detection helps radiologists in detecting abnormalities efficiently, researchers 
      across the world are striving to develop reliable models to deal with. One of the 
      common approaches to identifying breast cancer is through breast mammograms. 
      However, the identification of malignant breasts from mass lesions is a 
      challenging research problem. In the current work, we propose a method for the 
      classification of breast mass using mammograms which consists of two main stages. 
      At first, we extract deep features from the input mammograms using the well-known 
      VGG16 model while incorporating an attention mechanism into this model. Next, we 
      apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with 
      Adaptive Beta Hill Climbing based local search to obtain an optimal features 
      subset. The optimal features subset is fed to the K-nearest neighbors (KNN) 
      classifier for the classification. The proposed model is demonstrated to be very 
      useful for identifying and differentiating malignant and healthy breasts 
      successfully. For experimentation, we evaluate our model on the digital database 
      for screening mammography (DDSM) database and achieve 96.07% accuracy using only 
      25% of features extracted by the attention-aided VGG16 model. The Python code of 
      our research work is publicly available at: 
      https://github.com/Ppayel/BreastLocalSearchSSD.
CI  - (c) The Author(s) 2022.
FAU - Pramanik, Payel
AU  - Pramanik P
AD  - Department of Computer Science and Engineering, Jadavpur University, Kolkata, 
      India. GRID: grid.216499.1. ISNI: 0000 0001 0722 3459
FAU - Mukhopadhyay, Souradeep
AU  - Mukhopadhyay S
AD  - Department of Computer Science and Engineering, Jadavpur University, Kolkata, 
      India. GRID: grid.216499.1. ISNI: 0000 0001 0722 3459
FAU - Mirjalili, Seyedali
AU  - Mirjalili S
AUID- ORCID: 0000-0002-1443-9458
AD  - Centre for Artificial Intelligence Research and Optimisation, Torrens University 
      Australia, Brisbane, QLD 4006 Australia. GRID: grid.449625.8. ISNI: 0000 0004 
      4654 2104
AD  - Yonsei Frontier Lab, Yonsei University, Seoul, South Korea. GRID: grid.15444.30. 
      ISNI: 0000 0004 0470 5454
AD  - University Research and Innovation Center, Obuda University, Budapest, 1034 
      Hungary. GRID: grid.440535.3. ISNI: 0000 0001 1092 7422
FAU - Sarkar, Ram
AU  - Sarkar R
AD  - Department of Computer Science and Engineering, Jadavpur University, Kolkata, 
      India. GRID: grid.216499.1. ISNI: 0000 0001 0722 3459
LA  - eng
PT  - Journal Article
DEP - 20221105
PL  - England
TA  - Neural Comput Appl
JT  - Neural computing & applications
JID - 9313239
PMC - PMC9638217
OTO - NOTNLM
OT  - Algorithm
OT  - Breast cancer
OT  - Deep learning
OT  - Local search
OT  - Mammogram images
OT  - Optimization
OT  - Social ski-driver
COIS- Conflict of interestThe authors declare that there are no conflict of interests.
EDAT- 2022/11/15 06:00
MHDA- 2022/11/15 06:01
PMCR- 2022/11/05
CRDT- 2022/11/14 01:48
PHST- 2022/02/09 00:00 [received]
PHST- 2022/09/25 00:00 [accepted]
PHST- 2022/11/15 06:00 [pubmed]
PHST- 2022/11/15 06:01 [medline]
PHST- 2022/11/14 01:48 [entrez]
PHST- 2022/11/05 00:00 [pmc-release]
AID - 7895 [pii]
AID - 10.1007/s00521-022-07895-x [doi]
PST - ppublish
SO  - Neural Comput Appl. 2023;35(7):5479-5499. doi: 10.1007/s00521-022-07895-x. Epub 
      2022 Nov 5.