Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

Darweesh, M. Saeed; Adel, Mostafa; Anwar, Ahmed; Farag, Omar; Kotb, Ahmed; Adel, Mohamed; Tawfik, A.; Mostafa, Hassan

doi:10.1080/23311916.2021.1968324

Cited by 14 publications

(5 citation statements)

References 32 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Machine learning features are crucial in enhancing the accuracy and effectiveness of various algorithms and models (Darweesh et al, 2021). By incorporating machine learning features, algorithms can better analyze and interpret complex data patterns, improving decision-making and problem-solving capabilities (Wang et al, 2021).…”

Section: Machine Learning Featuresmentioning

confidence: 99%

Analysis and Simulation of Improved Seismic Data using Adaptive Processing by National Data Centre Iraq

Shamkhi,

Mahmood,

Salman

2023

Preprint

View full text Add to dashboard Cite

All States Parties have convenient access to all International Monitoring System (IMS) data, International Data Center (IDC) products, and all applications and scientific studies programs used in the IDC of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Integrating machine learning with seismic exploration is crucial for obtaining accurate and essential information about subsurface formations' stratigraphic structure, lithology, and porosity. However, there still needs to be a clear understanding of which algorithm produces the most accurate earthquake detection. Consequently, this study aims to perform a comparative analysis of the effectiveness of LSTM, CNN, MLP, and SVM algorithms in earthquake detection. This study has used various earthquake datasets from Arabian Sea earthquake seismic event on 26 October 2022 at 23:00:07 which was detected by two IMS monitoring technologies and non-IMS which is implemented in this work is the inclusion of analysis data using HA1 IMS (hydroacoustic station) with event location integration with seismic data for stations near that seismic event. As well as seismic event which was also studied and evaluated in Turkey, detected by IMS and non-IMS stations in IRIS on 23 November 2022 at 01:08:15 and analyzed the performance of each algorithm on these datasets by applying numerous performance metrics related to accuracy, precision, recall, F1-score, and others. The output performance results have demonstrated that the CNN network outperforms all other algorithms. In conclusion, this study provides a comprehensive evaluation of the existing literature on digital signal processing techniques employed in the analysis and detection of seismic waves.

show abstract

Section: Machine Learning Featuresmentioning

confidence: 99%

Analysis and Simulation of Improved Seismic Data using Adaptive Processing by National Data Centre Iraq

Shamkhi,

Mahmood,

Salman

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The whole process is carried out on the cloud for cross-validation from experts. Moreover, the patients from remote areas will be capable to input radiology images into the cloud system, which is [22] Xception and Deeplabv3+ 95+ Saba et al [20] AlexNet and DenseNet201 92.8 Mohiyuddin et al [21] YOLOv5 96.5 Darweesh et al [38] LBP + random forest 85 Yu et al [39] VGG16 89.06 Shi et al [40] CNN 83.6 Saba et al [15] Naive Bayesian and artificial neural network 98 Saraswathi et al [41] Swarm intelligence 92 further investigated through the CAD system located on the cloud. The proposed method has shown their significant ability to enhance accuracy and achieve remarkable performance on the classification task.…”

Section: Conclusion and Future Directionmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

Alyami

Sadad

Rehman

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.

show abstract

“…First Secara sederhana, data mining adalah penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah besar data [6]. Data mining juga dikenal sebagai serangkaian proses untuk mengeksplorasi nilai tambah dalam bentuk pengetahuan, di mana tidak dapat dilakukan secara manual diketahui dari kumpulan data.…”

Section: A Data Miningunclassified

Klasifikasi Penyakit Kanker Payudara Menggunakan Perbandingan Algoritma Machine Learning

Muntiari

Hanif²

2022

IKOMTI

View full text Add to dashboard Cite

Salah satu penyakit yang sangat ditakuti adalah kanker payudara, kanker payudara termasuk penyakit yang mematikan pada Wanita. Kanker payudara dikelompokkan menjadi dua jenis, yaitu ganas dan jinak. Dalam pengelompokkan jenis ini dibutuhkan metode yang cepat agar dapat membantu dalam pengambilan keputusan. Machine learning adalah bagian dari bidang kecerdasan buatan yang berfokus pada penerapan algoritma dan metode khusus untuk prediksi, pengenalan pola, dan klasifikasi. Sehingga Machine learning dapat membantu dalam mengelompokkan jenis kanker payudara. Penelitian ini menggunakan 7 algoritma yaitu neural network, decision tree, naïve bayes, k-nearest neighbor, logistic regresion, random forest, dan support vector machines dalam mengelompokkan jenis kanker payudara. Dengan pengolahan data menggunakan aplikasi RapidMiner didapat bahwa nilai akurasi dari algoritma logistic regresion, decision tree, naïve bayes dan k-nearest neighbor memiliki nilai akurasi yang sama tinggi yaitu sebesar 95,00%. Sehingga algoritma logistic regresion, decision tree, naïve bayes dan k-nearest neighbor mempercepat pengambilan keputusan dalam memprediksi dalam klasifikasi penentuan jenis kanker payudara.

show abstract

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

Cited by 14 publications

References 32 publications

Analysis and Simulation of Improved Seismic Data using Adaptive Processing by National Data Centre Iraq

Analysis and Simulation of Improved Seismic Data using Adaptive Processing by National Data Centre Iraq

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM)

Klasifikasi Penyakit Kanker Payudara Menggunakan Perbandingan Algoritma Machine Learning

Contact Info

Product

Resources

About