Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset and Image Acquisition
2.2. Image Color Normalization
2.3. Image Denoising
2.4. Breast Cancer Detection
2.4.1. KM Clustering Method
2.4.2. FCM Clustering
2.4.3. FKM Clustering
2.4.4. Optimization Design with KM++CSO
Algorithm 1: KM++CSO. |
|
2.4.5. Binary Image Detection
2.4.6. Improved Binary Image Using Mathematical Dilation Operators
2.4.7. Edge Detection
3. Results
3.1. Breast Cancer Detection Results
3.2. Manual Detection Versus Measurement
4. Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors | Methods | Dataset | SEN (%) | SPEC (%) | ACC (%) |
---|---|---|---|---|---|
Karabatak et al. [2] | Naïve Bayesian | Wisconsin breast cancer | 99.11 | 98.25 | 98.54 |
Pak et al. [3] | SVM and ANN | Mini–MIAS and DDSM | SVM = 100 | SVM = 80 | SVM = 92.85 |
ANN = 87.50 | ANN = 93.33 | ANN = 91.31 | |||
Yang et al. [5] | PSCSM | Mini–MIAS | – | – | 90.9 |
Punita et al. [6] | RGM, MRF segmentation, Stochastic Relaxation methods, and Fuzzy | DDSM | 98.10 | 97.8 | 98.00 |
Al-masni et al. [7] | Regional deep learning based on CNN and CLAHE | IN Breast | 97.14 | 92.41 | 95.64 |
Shen et al. [8] | MSGDM | IN Breast | 97.56 | 88.89 | 94.16 |
Punita et al. [9] | Fuzzy Rough Segmentation (FR), Rough Segmentation (RS), and Fuzzy Segmentation (FS) | Mini–MIAS | FR = 91.23 | FR = 91.23 | FR = 97.45 |
RS = 80.25 | RS = 79.45 | RS = 82.45 | |||
FS = 89.89 | FS = 89.12 | FS = 90.12 | |||
Zebari et al. [10] | The thresholding method and binary morphological for segmentation | Mini–MIAS, IN BCDR | 98.9 | 98.4 | 98.13 |
Anji et al. [11] | DNNS | MG Cancer | – | – | 97.21 |
Azlan et al. [12] | PCA and SVM | DDSM and Mini–MIAS | 93.94 | 96.61 | 95.24 |
George et al. [13] | The hybrid methods by combined the CNN and SVM | BreaKHis | 97.24 | 96.18 | 96.21 |
Alanazi et al. [14] | CNN architectures | Kaggle 162 H&E | - | - | 87.00 |
Mohammed et al. [15] | SVM | Mini–MIAS | - | - | 91.12 |
Agnes et al. [16] | MA-CNN | Mini–MIAS | 96.00 | - | - |
Yektaei et al. [17] | Multiscale Convolutional Neural Network (MCNN) | Mini–MIAS | 95.90 | - | 97.03 |
Kaur et al. [18] | Multiclass Support Vector Machine (MSVM) | Mini–MIAS | - | - | 96.90 |
Viswanath et al. [19] | SVM | Raw sample images | – | 68 | 84.84 |
Jin et al. [20] | Binary classifier with CNNI–BCC | Mini–MIAS | – | – | 73.24 |
Kayode et al. [21] | SVM | Mini–MIAS | 94.4 | 91.3 | – |
Debelee et al. [22] | SVM and MLP | Mini–MIAS | SVM = 99.48 | SVM = 98.16 | SVM = 99 |
MLP = 97.40 | MLP = 96.26 | MLP = 97 | |||
Zhang et al. [23] | Mask R–CNN | DCE–MRI | 80 | 74 | 75 |
Methods | Datasets | Percentage | |||
---|---|---|---|---|---|
SEN (%) | SPEC (%) | ACC (%) | Jaccard Index (%) | ||
KM | DDSM | 88.38 | 89.10 | 88.89 | 80.45 (2108/2620) |
FCM | 67.22 | 69.12 | 69.10 | 70.95 (1859/2620) | |
FKM | 90.15 | 90.18 | 90.16 | 83.55 (2189/2620) | |
KM++CSO | 95.68 | 95.10 | 95.49 | 87.37 (2289/2620) | |
KM | Mini–MIAS | 90.10 | 89.78 | 89.92 | 85.13 (275/323) |
FCM | 60.67 | 60.97 | 60.78 | 64.08 (207/323) | |
FKM | 91.12 | 91.20 | 91.14 | 86.99 (281/323) | |
KM++CSO | 96.78 | 97.10 | 96.92 | 89.47 (289/323) | |
KM | BCDR | 89.96 | 89.89 | 89.92 | 78.86 (97/123) |
FCM | 61.29 | 61.19 | 61.20 | 65.85 (81/123) | |
FKM | 91.40 | 91.56 | 91.48 | 88.62 (109/123) | |
KM++CSO | 96.90 | 96.16 | 96.42 | 91.05 (112/123) |
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Wisaeng, K. Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization. Diagnostics 2022, 12, 3088. https://doi.org/10.3390/diagnostics12123088
Wisaeng K. Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization. Diagnostics. 2022; 12(12):3088. https://doi.org/10.3390/diagnostics12123088
Chicago/Turabian StyleWisaeng, Kittipol. 2022. "Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization" Diagnostics 12, no. 12: 3088. https://doi.org/10.3390/diagnostics12123088
APA StyleWisaeng, K. (2022). Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization. Diagnostics, 12(12), 3088. https://doi.org/10.3390/diagnostics12123088