Effect of image binarization thresholds on breast cancer identification in mammography images using OTSU, Niblack, Burnsen, Thepade's SBTC

Khairnar, Smita; Thepade, Sudeep D.; Gite, Shilpa

doi:10.1016/j.iswa.2021.200046

Cited by 32 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In ( 30 , 31 ), the use of VGG16 was modified to classify microcalcification images into benign or malignant cases from the DDSM database and obtained accuracy of 94.3 and 87.0%, respectively. Study of ( 33 ) utilized AlexNet and managed to achieve an accuracy of 79.1% upon utilizing 10-fold cross validation technique with 300 epochs and learning rate of 0.01 based on 900 images from SYUCC and NAHSMU database. In this research, the technique of cross validation was not performed, but the accuracy achieved in AlexNet is much higher, reaching 83.1% with just 30 epochs.…”

Section: Resultsmentioning

confidence: 99%

“…The optimum threshold value is identified to achieve the best performance in distinguishing the target class from the background class, which is mostly utilized in mammography image binarization. Before executing the procedures for breast cancer detection segmentation and feature extraction, this thresholding approach is employed as a pre-processing technique ( 32 , 33 ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis

Leong

Hasikin‬

Lai

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Breast cancer is among the most common types of cancer in women and under the cases of misdiagnosed, or delayed in treatment, the mortality risk is high. The existence of breast microcalcifications is common in breast cancer patients and they are an effective indicator for early sign of breast cancer. However, microcalcifications are often missed and wrongly classified during screening due to their small sizes and indirect scattering in mammogram images. Motivated by this issue, this project proposes an adaptive transfer learning deep convolutional neural network in segmenting breast mammogram images with calcifications cases for early breast cancer diagnosis and intervention. Mammogram images of breast microcalcifications are utilized to train several deep neural network models and their performance is compared. Image filtering of the region of interest images was conducted to remove possible artifacts and noises to enhance the quality of the images before the training. Different hyperparameters such as epoch, batch size, etc were tuned to obtain the best possible result. In addition, the performance of the proposed fine-tuned hyperparameter of ResNet50 is compared with another state-of-the-art machine learning network such as ResNet34, VGG16, and AlexNet. Confusion matrices were utilized for comparison. The result from this study shows that the proposed ResNet50 achieves the highest accuracy with a value of 97.58%, followed by ResNet34 of 97.35%, VGG16 96.97%, and finally AlexNet of 83.06%.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis

Leong

Hasikin‬

Lai

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Let the iris image be I (r,c) of size r × c pixels, grayscale. The TSBTC [29,30] feature vector of N-ary may be considered as [T1, T2, Tn]. Here, Ti indicates the i th cluster centroids of the grayscale image using TSBTC N-ary.…”

Section: Tsbtcmentioning

confidence: 99%

Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features

Khade

Gite

Thepade

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.

show abstract

“…It is possible to identify this disease using various methods, including a biopsy, ultrasound, thermography, and fine-needle aspiration biopsies [3]. While mammography has been the primary mode of early detection, it is not always enough for doctors to conclude whether or not the patient has breast cancer [4], [5]. As a result, the detection rate is just (60-70)% accurate [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison of breast cancer classification models on Wisconsin dataset

Kadhim

Kamil

2022

IJRES

View full text Add to dashboard Cite

Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.

show abstract

Effect of image binarization thresholds on breast cancer identification in mammography images using OTSU, Niblack, Burnsen, Thepade's SBTC

Cited by 32 publications

References 15 publications

Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis

Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis

Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features

Comparison of breast cancer classification models on Wisconsin dataset

Contact Info

Product

Resources

About