Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection
Source abstract
Abstract
Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of various techniques, where the fusion of deep learning (DL), traditional machine learning (ML) techniques, and enhanced classification models have been deployed using the curated dataset. The analysis outcome proves that the proposed enhanced RF (ERF), enhanced DT (EDT) and enhanced LR (ELR) models for BC detection outperformed most of the existing models with impressive results.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Radiomics and Machine Learning in Medical Imaging · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (66)
Show source-provided references
1. Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features10.1109/ACCESS.2019.2892795 ↗
2. Deep-learning-empowered breast cancer auxiliary diagnosis for 5GB remote e-health10.1109/MWC.001.2000374 ↗
3. Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review10.1016/j.media.2021.102049 ↗
4. A systematic survey of deep learning in breast cancer10.1002/int.22622 ↗
5. A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique10.1109/ACCESS.2021.3079204 ↗
6. On combining biclustering mining and AdaBoost for breast tumor classification10.1109/TKDE.2019.2891622 ↗
7. A deep learning mammography-based model for improved breast cancer risk prediction10.1148/radiol.2019182716 ↗
8. Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer10.1097/RLI.0000000000000358 ↗
9. Deep learning to improve breast cancer detection on screening mammography10.1038/s41598-019-48995-4 ↗
10. Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction10.1148/radiol.2019190872 ↗
11. A hybrid approach for breast cancer classification and diagnosis
12. Mammogram classification using contourlet features with forest optimization-based feature selection approach10.1007/s11042-018-5804-0 ↗
13. A comparison of different Gabor feature extraction approaches for mass classification in mammography10.1007/s11042-015-3017-3 ↗
14. A support vector machine approach for detection of microcalcifications10.1109/TMI.2002.806569 ↗
15. Multi-view feature fusion based four views model for mammogram classification using convolutional neural network10.1109/ACCESS.2019.2953318 ↗
16. Multi-view information fusion in mammograms: A comprehensive overview10.1016/j.inffus.2019.05.001 ↗
17. A framework for breast cancer classification using multi-DCNNs10.1016/j.compbiomed.2021.104245 ↗
18. Multi-modal advanced deep learning architectures for breast cancer survival prediction10.1016/j.knosys.2021.106965 ↗
19. Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion10.3390/s22030807 ↗
20. Toward robust mammography-based models for breast cancer risk10.1126/scitranslmed.aba4373 ↗
21. Basic of machine learning and deep learning in imaging for medical physicists10.1016/j.ejmp.2021.03.026 ↗
22. Feature selection and classification in breast cancer prediction using IoT and machine learning10.1016/j.measurement.2021.109442 ↗
23. Breast cancer detection by leveraging machine learning10.1016/j.icte.2020.04.009 ↗
24. Breast cancer diagnosis using cluster-based undersampling and boosted C5. 0 algorithm10.1007/s12555-019-1061-x ↗
25. Ensemble deep-learning-enabled clinical decision support system for breast cancer diagnosis and classification on ultrasound images10.3390/biology11030439 ↗
26. Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network10.1007/s12065-020-00403-x ↗
27. Machine-learning-based disease diagnosis: A comprehensive review10.3390/healthcare10030541 ↗
28. Automated breast cancer detection models based on transfer learning10.3390/s22030876 ↗
29. An improved random forest-based rule extraction method for breast cancer diagnosis10.1016/j.asoc.2019.105941 ↗
30. Predicting breast cancer biopsy outcomes from BI-RADS findings using random forests with chi-square and MI features
31. Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers10.1007/s00500-019-04066-4 ↗
32. Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree10.1007/s42452-020-2575-9 ↗
33. Hierarchical fused model with deep learning and type-2 fuzzy learning for breast cancer diagnosis10.1109/TFUZZ.2020.3013681 ↗
34. Breast cancer classification from histopathological images using patch-based deep learning modeling10.1109/ACCESS.2021.3056516 ↗
35. Classification of breast mass in two-view mammograms via deep learning10.1049/ipr2.12035 ↗
36. Deep learning modeling using normal mammograms for predicting breast cancer risk10.1002/mp.13886 ↗
37. Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN10.1002/mp.14569 ↗
38. Breast cancer histopathological image classification using attention high-order deep network10.1002/ima.22628 ↗
39. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning10.1016/j.patrec.2019.03.022 ↗
40. Transfer learning in breast cancer diagnoses via ultrasound imaging10.3390/cancers13040738 ↗
41. Radiomic feature reduction approach to predict breast cancer by contrast-enhanced spectral mammography images10.3390/diagnostics11040684 ↗
42. SPAER: Sparse deep convolutional autoencoder model to extract low dimensional imaging biomarkers for early detection of breast cancer using dynamic thermography10.3390/app11073248 ↗
43. Triphasic DeepBRCA–A deep learning-based framework for identification of biomarkers for breast cancer stratification10.1109/ACCESS.2021.3093616 ↗
44. A novel voting convergent difference neural network for diagnosing breast cancer10.1016/j.neucom.2021.01.083 ↗
45. Tree-based and machine learning algorithm analysis for breast cancer classification
46. Breast cancer detection using convolutional neural networks for mammogram imaging system
47. Diagnosis of breast cancer based on modern mammography using hybrid transfer learning10.1007/s11045-020-00756-7 ↗
48. Hybrid deep transfer learning of CNN-based LR-PCA for breast lesion diagnosis via medical breast mammograms10.3390/s22134938 ↗
49. Mammographic classification based on XGBoost and dcnn with multi features10.1109/ACCESS.2020.2986546 ↗
50. Stacked deep dense neural network model to predict Alzheimer’s dementia using audio transcript data10.1109/ACCESS.2022.3161749 ↗
51. Automated early breast cancer detection and classification system10.1007/s11760-021-01882-w ↗
52. Early detection of breast cancer using hybrid of series network and VGG-1610.1007/s11042-020-09914-2 ↗
53. Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing10.1016/j.bspc.2020.102341 ↗
54. An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson’s disease classification10.1016/j.bspc.2021.102452 ↗
55. DE-Ada: A novel model for breast mass classification using cross-modal pathological semantic mining and organic integration of multi-feature fusions10.1016/j.ins.2020.05.080 ↗
56. DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques10.1016/j.chemolab.2022.104562 ↗
57. Machine learning concepts and its applications for prediction of diseases based on drug behaviour: An extensive review10.1016/j.chemolab.2022.104637 ↗
58. Decision-tree-initialized dendritic neuron model for fast and accurate data classification
59. “DDSM Mammography.” Accessed: Jul. 09, 2023. [Online]. Available: https://www.kaggle.com/skooch/ddsm-mammography/
60. Bio-imaging-based machine learning algorithm for breast cancer detection10.3390/diagnostics12051134 ↗
61. A cloud-based predictive model for the detection of breast cancer
62. Transfer learning and fine tuning in breast mammogram abnormalities classification on CBIS-DDSM database10.25046/aj050220 ↗
63. A novel algorithm for breast mass classification in digital mammography based on feature fusion10.1155/2020/8860011 ↗
64. Effective extraction of Gabor features for false positive reduction and mass classification in mammography
65. Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution10.1016/j.eswa.2016.08.070 ↗
66. DeepCAD: A computer-aided diagnosis system for mammographic masses using deep invariant features10.3390/computers5040028 ↗