194 related articles for article (PubMed ID: 36766453)
1. A Novel Medical Image Enhancement Algorithm for Breast Cancer Detection on Mammography Images Using Machine Learning.
Avcı H; Karakaya J
Diagnostics (Basel); 2023 Jan; 13(3):. PubMed ID: 36766453
[TBL] [Abstract][Full Text] [Related]
2. Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.
Jebamony J; Jacob D
Curr Med Imaging; 2020; 16(6):703-710. PubMed ID: 32723242
[TBL] [Abstract][Full Text] [Related]
3. Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.
Kashyap KL; Bajpai MK; Khanna P; Giakos G
Int J Numer Method Biomed Eng; 2018 Jan; 34(1):. PubMed ID: 28603939
[TBL] [Abstract][Full Text] [Related]
4. A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.
Jones MA; Sadeghipour N; Chen X; Islam W; Zheng B
Med Phys; 2023 Dec; 50(12):7670-7683. PubMed ID: 37083190
[TBL] [Abstract][Full Text] [Related]
5. A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.
Samee NA; Alhussan AA; Ghoneim VF; Atteia G; Alkanhel R; Al-Antari MA; Kadah YM
Sensors (Basel); 2022 Jun; 22(13):. PubMed ID: 35808433
[TBL] [Abstract][Full Text] [Related]
6. A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer.
Hazarika M; Mahanta LB
Asian Pac J Cancer Prev; 2018 Aug; 19(8):2141-2148. PubMed ID: 30139217
[TBL] [Abstract][Full Text] [Related]
7. An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm.
Chowdhary CL; Mittal M; P K; Pattanaik PA; Marszalek Z
Sensors (Basel); 2020 Jul; 20(14):. PubMed ID: 32668793
[TBL] [Abstract][Full Text] [Related]
8. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.
Premaladha J; Ravichandran KS
J Med Syst; 2016 Apr; 40(4):96. PubMed ID: 26872778
[TBL] [Abstract][Full Text] [Related]
9. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
10. Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.
Keller BM; Nathan DL; Wang Y; Zheng Y; Gee JC; Conant EF; Kontos D
Med Phys; 2012 Aug; 39(8):4903-17. PubMed ID: 22894417
[TBL] [Abstract][Full Text] [Related]
11. Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography.
Wang J; Nishikawa RM; Yang Y
Med Phys; 2017 Jul; 44(7):3726-3738. PubMed ID: 28477395
[TBL] [Abstract][Full Text] [Related]
12. Comparing the performance of mammographic enhancement algorithms: a preference study.
Sivaramakrishna R; Obuchowski NA; Chilcote WA; Cardenosa G; Powell KA
AJR Am J Roentgenol; 2000 Jul; 175(1):45-51. PubMed ID: 10882244
[TBL] [Abstract][Full Text] [Related]
13. Technique for preprocessing of digital mammogram.
Maitra IK; Nag S; Bandyopadhyay SK
Comput Methods Programs Biomed; 2012 Aug; 107(2):175-88. PubMed ID: 21669471
[TBL] [Abstract][Full Text] [Related]
14. Comparing the performance of image enhancement methods to detect microcalcification clusters in digital mammography.
Moradmand H; Setayeshi S; Karimian AR; Sirous M; Akbari ME
Iran J Cancer Prev; 2012; 5(2):61-8. PubMed ID: 25628822
[TBL] [Abstract][Full Text] [Related]
15. Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution.
Pak F; Kanan HR; Alikhassi A
Comput Methods Programs Biomed; 2015 Nov; 122(2):89-107. PubMed ID: 26206406
[TBL] [Abstract][Full Text] [Related]
16. Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN.
Alshamrani K; Alshamrani HA; Alqahtani FF; Almutairi BS
Sensors (Basel); 2022 Dec; 23(1):. PubMed ID: 36616832
[TBL] [Abstract][Full Text] [Related]
17. Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features.
Jones MA; Faiz R; Qiu Y; Zheng B
Phys Med Biol; 2022 Feb; 67(5):. PubMed ID: 35130517
[No Abstract] [Full Text] [Related]
18. Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features.
Cruz-Ramos C; García-Avila O; Almaraz-Damian JA; Ponomaryov V; Reyes-Reyes R; Sadovnychiy S
Entropy (Basel); 2023 Jun; 25(7):. PubMed ID: 37509938
[TBL] [Abstract][Full Text] [Related]
19. Comparison of enhancement methods for mammograms with performance measures.
Kurt B; Nabiyev VV; Turhan K
Stud Health Technol Inform; 2014; 205():486-90. PubMed ID: 25160232
[TBL] [Abstract][Full Text] [Related]
20. Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm.
Jiang J; Peng J; Hu C; Jian W; Wang X; Liu W
Artif Intell Med; 2022 Dec; 134():102419. PubMed ID: 36462904
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
