Matches in SemOpenAlex for { <https://semopenalex.org/work/W4324149689> ?p ?o ?g. }
- W4324149689 endingPage "24" @default.
- W4324149689 startingPage "1" @default.
- W4324149689 abstract "Breast cancer in women is the most frequently diagnosed and major leading cause of cancer deaths. Due to the complex nature of microcalcification and masses, radiologists fail to diagnose breast cancer properly. In this research paper, we have employed a novel Deep Convolutional Neural Network (DCNN) model using a transfer learning strategy and compared the results with Machine Learning (ML) techniques such as Support vector machine (SVM) kernels and Decision Trees based on different features extracting strategies to distinguish cancer mammograms from normal subjects. In this study, we first extracted the hand-crafted features such as as texture, morphological, entropy-based, scale-invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) and fed into machine learning algorithm for classification. We then utilized the deep learning algorithms with transfer learning approach. The deep learning models yielded the highest detection performance with default and optimized parameters i.e. GoogleNet yielded accuracy (99.26%), AUC (0.9998) with default parameters and AlexNet yielded accuracy (99.26%), AUC (0.9996) with optimized parameters. The results reveal that proposed approach is more robust for early detection of breast mammograms which can be best utilized for improved diagnosis and prognosis." @default.
- W4324149689 created "2023-03-15" @default.
- W4324149689 creator A5002634875 @default.
- W4324149689 creator A5008953932 @default.
- W4324149689 creator A5031983184 @default.
- W4324149689 creator A5051455386 @default.
- W4324149689 creator A5064562623 @default.
- W4324149689 creator A5070543103 @default.
- W4324149689 creator A5076999947 @default.
- W4324149689 creator A5081215670 @default.
- W4324149689 date "2023-03-14" @default.
- W4324149689 modified "2023-10-01" @default.
- W4324149689 title "Deep convolutional neural networks accurately predict breast cancer using mammograms" @default.
- W4324149689 cites W1519122895 @default.
- W4324149689 cites W1806891645 @default.
- W4324149689 cites W1830454849 @default.
- W4324149689 cites W1964168965 @default.
- W4324149689 cites W1967426304 @default.
- W4324149689 cites W1978167298 @default.
- W4324149689 cites W1980527762 @default.
- W4324149689 cites W1982575342 @default.
- W4324149689 cites W1988452762 @default.
- W4324149689 cites W2004227461 @default.
- W4324149689 cites W2034365297 @default.
- W4324149689 cites W2059331675 @default.
- W4324149689 cites W2061715187 @default.
- W4324149689 cites W2093400823 @default.
- W4324149689 cites W2094693600 @default.
- W4324149689 cites W2095925574 @default.
- W4324149689 cites W2104737162 @default.
- W4324149689 cites W2106787323 @default.
- W4324149689 cites W2107062464 @default.
- W4324149689 cites W2108009806 @default.
- W4324149689 cites W2108598243 @default.
- W4324149689 cites W2109597745 @default.
- W4324149689 cites W2117403911 @default.
- W4324149689 cites W2118291747 @default.
- W4324149689 cites W2133911584 @default.
- W4324149689 cites W2137602680 @default.
- W4324149689 cites W2145388359 @default.
- W4324149689 cites W2145517326 @default.
- W4324149689 cites W2152744797 @default.
- W4324149689 cites W2165698076 @default.
- W4324149689 cites W2169388041 @default.
- W4324149689 cites W2169949947 @default.
- W4324149689 cites W2183341477 @default.
- W4324149689 cites W2207251655 @default.
- W4324149689 cites W2253429366 @default.
- W4324149689 cites W2284539364 @default.
- W4324149689 cites W2293009534 @default.
- W4324149689 cites W2299565249 @default.
- W4324149689 cites W2465621303 @default.
- W4324149689 cites W2494536949 @default.
- W4324149689 cites W2510224130 @default.
- W4324149689 cites W2522700847 @default.
- W4324149689 cites W2523168882 @default.
- W4324149689 cites W2546302380 @default.
- W4324149689 cites W2547944663 @default.
- W4324149689 cites W2551940739 @default.
- W4324149689 cites W2556605533 @default.
- W4324149689 cites W2561981131 @default.
- W4324149689 cites W2562395590 @default.
- W4324149689 cites W2573215997 @default.
- W4324149689 cites W2575615142 @default.
- W4324149689 cites W2577696039 @default.
- W4324149689 cites W2618530766 @default.
- W4324149689 cites W2735065599 @default.
- W4324149689 cites W2773413394 @default.
- W4324149689 cites W2785207037 @default.
- W4324149689 cites W2791915981 @default.
- W4324149689 cites W2792379403 @default.
- W4324149689 cites W2793701907 @default.
- W4324149689 cites W2803080351 @default.
- W4324149689 cites W2805219681 @default.
- W4324149689 cites W2905481768 @default.
- W4324149689 cites W2911188335 @default.
- W4324149689 cites W2912545514 @default.
- W4324149689 cites W2919115771 @default.
- W4324149689 cites W2947587948 @default.
- W4324149689 cites W2964189045 @default.
- W4324149689 cites W2964242896 @default.
- W4324149689 cites W41027960 @default.
- W4324149689 cites W4224254448 @default.
- W4324149689 cites W4250589301 @default.
- W4324149689 doi "https://doi.org/10.1080/17455030.2023.2189485" @default.
- W4324149689 hasPublicationYear "2023" @default.
- W4324149689 type Work @default.
- W4324149689 citedByCount "0" @default.
- W4324149689 crossrefType "journal-article" @default.
- W4324149689 hasAuthorship W4324149689A5002634875 @default.
- W4324149689 hasAuthorship W4324149689A5008953932 @default.
- W4324149689 hasAuthorship W4324149689A5031983184 @default.
- W4324149689 hasAuthorship W4324149689A5051455386 @default.
- W4324149689 hasAuthorship W4324149689A5064562623 @default.
- W4324149689 hasAuthorship W4324149689A5070543103 @default.
- W4324149689 hasAuthorship W4324149689A5076999947 @default.
- W4324149689 hasAuthorship W4324149689A5081215670 @default.
- W4324149689 hasConcept C108583219 @default.