Matches in SemOpenAlex for { <https://semopenalex.org/work/W4307649548> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W4307649548 endingPage "92" @default.
- W4307649548 startingPage "85" @default.
- W4307649548 abstract "Cancer in breast is the most prevalent disease discovered in women’s breast cells, and it can also lead to death. Early detection and diagnosis help to reduce this mortality rate. Different Artificial Intelligent (AI) techniques such Machine Learning (ML), Deep Learning (DL) is being used in the medical industry to predict breast cancer. Different breast cancer images like mammogram, ultrasound and biopsy, etc. are used for analysis. This work aims to provide deep Convolutional Neural Network (CNN) architecture to analyze breast cancer through mammogram images. Nowadays, due to great performance deep CNN is widely used. In this paper, we presented CNN model with five layers of convolutional, five layers of max pooling, four layers of dropout and two layers of fully connected. CNN is developed using breast image dataset DDSM which is publicly available. Proposed CNN model has achieved 89.46% accuracy for breast mass classification as benign and malignant." @default.
- W4307649548 created "2022-11-04" @default.
- W4307649548 creator A5000740095 @default.
- W4307649548 creator A5017233455 @default.
- W4307649548 creator A5058353750 @default.
- W4307649548 creator A5084078367 @default.
- W4307649548 date "2022-10-28" @default.
- W4307649548 modified "2023-09-27" @default.
- W4307649548 title "Breast Mass Classification Using Convolutional Neural Network" @default.
- W4307649548 cites W1822639386 @default.
- W4307649548 cites W1901616594 @default.
- W4307649548 cites W2024549356 @default.
- W4307649548 cites W2492863677 @default.
- W4307649548 cites W2558340819 @default.
- W4307649548 cites W2575657035 @default.
- W4307649548 cites W2606880313 @default.
- W4307649548 cites W2745869571 @default.
- W4307649548 cites W2767236661 @default.
- W4307649548 cites W2768529727 @default.
- W4307649548 cites W2793701907 @default.
- W4307649548 cites W2793956967 @default.
- W4307649548 cites W2919115771 @default.
- W4307649548 cites W2964189045 @default.
- W4307649548 cites W2972934778 @default.
- W4307649548 cites W2980472724 @default.
- W4307649548 cites W2986012168 @default.
- W4307649548 cites W3024448780 @default.
- W4307649548 cites W3033750579 @default.
- W4307649548 cites W3153269770 @default.
- W4307649548 cites W3216178377 @default.
- W4307649548 cites W4205229383 @default.
- W4307649548 cites W4205581135 @default.
- W4307649548 cites W624838065 @default.
- W4307649548 doi "https://doi.org/10.1007/978-981-19-4863-3_8" @default.
- W4307649548 hasPublicationYear "2022" @default.
- W4307649548 type Work @default.
- W4307649548 citedByCount "0" @default.
- W4307649548 crossrefType "book-chapter" @default.
- W4307649548 hasAuthorship W4307649548A5000740095 @default.
- W4307649548 hasAuthorship W4307649548A5017233455 @default.
- W4307649548 hasAuthorship W4307649548A5058353750 @default.
- W4307649548 hasAuthorship W4307649548A5084078367 @default.
- W4307649548 hasConcept C108583219 @default.
- W4307649548 hasConcept C119857082 @default.
- W4307649548 hasConcept C121608353 @default.
- W4307649548 hasConcept C126322002 @default.
- W4307649548 hasConcept C153180895 @default.
- W4307649548 hasConcept C154945302 @default.
- W4307649548 hasConcept C2776145597 @default.
- W4307649548 hasConcept C2777423100 @default.
- W4307649548 hasConcept C2780472235 @default.
- W4307649548 hasConcept C41008148 @default.
- W4307649548 hasConcept C50644808 @default.
- W4307649548 hasConcept C530470458 @default.
- W4307649548 hasConcept C70437156 @default.
- W4307649548 hasConcept C71924100 @default.
- W4307649548 hasConcept C81363708 @default.
- W4307649548 hasConceptScore W4307649548C108583219 @default.
- W4307649548 hasConceptScore W4307649548C119857082 @default.
- W4307649548 hasConceptScore W4307649548C121608353 @default.
- W4307649548 hasConceptScore W4307649548C126322002 @default.
- W4307649548 hasConceptScore W4307649548C153180895 @default.
- W4307649548 hasConceptScore W4307649548C154945302 @default.
- W4307649548 hasConceptScore W4307649548C2776145597 @default.
- W4307649548 hasConceptScore W4307649548C2777423100 @default.
- W4307649548 hasConceptScore W4307649548C2780472235 @default.
- W4307649548 hasConceptScore W4307649548C41008148 @default.
- W4307649548 hasConceptScore W4307649548C50644808 @default.
- W4307649548 hasConceptScore W4307649548C530470458 @default.
- W4307649548 hasConceptScore W4307649548C70437156 @default.
- W4307649548 hasConceptScore W4307649548C71924100 @default.
- W4307649548 hasConceptScore W4307649548C81363708 @default.
- W4307649548 hasLocation W43076495481 @default.
- W4307649548 hasOpenAccess W4307649548 @default.
- W4307649548 hasPrimaryLocation W43076495481 @default.
- W4307649548 hasRelatedWork W2517027266 @default.
- W4307649548 hasRelatedWork W2738221750 @default.
- W4307649548 hasRelatedWork W2758063741 @default.
- W4307649548 hasRelatedWork W4206716432 @default.
- W4307649548 hasRelatedWork W4295190261 @default.
- W4307649548 hasRelatedWork W4307649548 @default.
- W4307649548 hasRelatedWork W4311257506 @default.
- W4307649548 hasRelatedWork W4321369474 @default.
- W4307649548 hasRelatedWork W4327499916 @default.
- W4307649548 hasRelatedWork W2551883569 @default.
- W4307649548 isParatext "false" @default.
- W4307649548 isRetracted "false" @default.
- W4307649548 workType "book-chapter" @default.