Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm

被引:3
|
作者
Thirumalaisamy, Selvakumar [1 ]
Thangavilou, Kamaleshwar [2 ]
Rajadurai, Hariharan [3 ]
Saidani, Oumaima [4 ]
Alturki, Nazik [4 ]
Mathivanan, Sandeep kumar [5 ]
Jayagopal, Prabhu [6 ]
Gochhait, Saikat [7 ,8 ]
机构
[1] Dr Mahalingam Coll Engn & Technol, Dept Artificial intelligence & Data Sci, Pollachi 642003, India
[2] Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T, Dept Comp Sci & Engn, Chennai 600062, India
[3] VIT Bhopal Univ, Sch Comp Sci & Engn, Indore Hwy Kothrikalan, Bhopal, India
[4] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[5] Galgotias Univ, Sch Comp Sci & Engn, Greater Noida 203201, India
[6] Vellore Inst Technol, Sch Comp Sci Engn & Informat Syst, Vellore 632014, India
[7] Constituent Symbiosis Int Deemed Univ, Symbiosis Inst Digital & Telecom Management, Pune 412115, India
[8] Samara State Med Univ, Neurosci Res Inst, Samara 443001, Russia
关键词
transfer learning; breast cancer; convolutional neural network; Ant Colony Optimization; ResNet101; hyperparameters; ANT COLONY OPTIMIZATION;
D O I
10.3390/diagnostics13182925
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO-ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO-ResNet101 over current methodologies.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Breast Cancer Detection and Classification using Deep Learning Xception Algorithm
    Abunasser, Basem S.
    AL-Hiealy, Mohammed Rasheed J.
    Zaqout, Ihab S.
    Abu-Naser, Samy S.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (07) : 223 - 228
  • [2] Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning
    Gurumoorthy, Ramalingam
    Kamarasan, Mari
    [J]. ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2024, 14 (01) : 12831 - 12836
  • [3] Breast Cancer Classification Using Deep Learning
    Jasmir
    Nurmaini, Siti
    Malik, Reza Firsandaya
    Abidin, Dodo Zaenal
    Zarkasi, Ahmad
    Kunang, Yesi Novaria
    Firdaus
    [J]. 2018 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (ICECOS), 2018, : 237 - 241
  • [4] Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer
    Chakravarthy, S. R. Sannasi
    Bharanidharan, N.
    Rajaguru, H.
    [J]. IRBM, 2023, 44 (03)
  • [5] A new parallel deep learning algorithm for breast cancer classification
    Kazemi, Ahmad
    Shiri, Mohammad Ebrahim
    Sheikhahmadi, Amir
    Khodamoradi, Mohamad
    [J]. INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 2021, 12 : 1269 - +
  • [6] Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model
    Kim, Chang-Min
    Park, Roy C.
    Hong, Ellen J.
    [J]. IEEE ACCESS, 2020, 8 : 197312 - 197323
  • [7] Classification of Breast Cancer Histology Using Deep Learning
    Golatkar, Aditya
    Anand, Deepak
    Sethi, Amit
    [J]. IMAGE ANALYSIS AND RECOGNITION (ICIAR 2018), 2018, 10882 : 837 - 844
  • [8] An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer
    Sami, Joy Christy Antony
    Arumugam, Umamakeswari
    [J]. MIDDLE EAST JOURNAL OF CANCER, 2024, 15 (01) : 40 - 51
  • [9] Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data
    Joshi, Amol Avinash
    Aziz, Rabia Musheer
    [J]. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2024, 34 (02)
  • [10] Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning
    Mohamed, Nuzaiha
    Almutairi, Reem Lafi
    Abdelrahim, Sayda
    Alharbi, Randa
    Alhomayani, Fahad Mohammed
    Elamin Elnaim, Bushra M.
    Elhag, Azhari A.
    Dhakal, Rajendra
    [J]. CANCERS, 2024, 16 (01)