Biomedical Mammography Image Classification Using Patches-Based Feature Engineering with Deep Learning and Ensemble Classifier
- Title
- Biomedical Mammography Image Classification Using Patches-Based Feature Engineering with Deep Learning and Ensemble Classifier
- Creator
- Poonia R.C.; Upreti K.; Jafri S.; Parashar J.; Vats P.; Singh J.
- Description
- In order to reduce the expense of radiologists, deep learning algorithms have recently been used in the mammograms screening field. Deep learning-based methods, like a Convolutional Neural Network (CNN), are now being used to categorize breast lumps. When it involves classifying mammogram imagery, CNN-based systems clearly outperform machine learning-based systems, but they do have certain disadvantages as well. Additional challenges include a dearth of knowledge on feature engineering and the impossibility of feature analysis for the existing patches of pictures, which are challenging to distinguish in low-contrast mammograms. Inaccurate patch assessments, higher calculation costs, inaccurate patch examinations, and non-recovered patched intensity variation are all results of mammogram image patches. This led to evidence that a CNN-based technique for identifying breast masses had poor classification accuracy. Deep Learning-Based Featured Reconstruction is a novel breast mass classification technique that boosts precision on low-contrast pictures (DFN). This system uses random forest boosting techniques together with CNN architectures like VGG 16 and Resnet 50 to characterize breast masses. Using two publicly accessible datasets of mammographic images, the suggested DFN approach is also contrasted with modern classification methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 275-285.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Biomedical images; Breast mass classification technique; CNN; Deep learning; Mammography
- Coverage
- Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; Jafri S., Administrative Science Department, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India; Vats P., Department of Computer Science and Engineering, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303164812-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Poonia R.C.; Upreti K.; Jafri S.; Parashar J.; Vats P.; Singh J., “Biomedical Mammography Image Classification Using Patches-Based Feature Engineering with Deep Learning and Ensemble Classifier,” CHRIST (Deemed To Be University) Institutional Repository, accessed March 1, 2025, https://archives.christuniversity.in/items/show/19363.