Application of machine learning and image processing for detection of breast cancer

Kashif, Muhammad; Malik, Kaleem Razzaq; Jabbar, Sohail; Chaudhry, Junaid

doi:10.1016/b978-0-12-819043-2.00006-x

Cited by 19 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This system is segmented into two parts in which the features are retrieved using the deep convolutional network and support vector machines are used for obtaining better accuracy. Kashif et al [ 31 ] suggested a hybrid model for predicting breast cancer from mammography images. First, the images were segmented and the features were extracted using mammogram processing and then classification was performed using the extracted features.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

show abstract

Section: Related Workmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, these studies do not include the results of other evaluation metrics such as precision, F1-score, and MCC, giving a better explanation for the classifier's performance on different classes. The proposed approach outperforms (Kashif, 2020) in the precision of abnormal, the recall of normal, and F1-score of abnormal while (Kashif, 2020) exceeds our approach in the accuracy. The proposed approach exceeds (Saber et al, 2021) only on the recall of the normal.…”

Section: (A)comparisonmentioning

confidence: 71%

“…In (Kashif, 2020), the authors have applied a 2D median filter to remove the noises from the images of the MIAS dataset. The dataset is divided into two classes: normal and abnormal (Benign and Malignant).…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

Breast cancer constitutes a significant threat to women's health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes

show abstract

“…Although extensive efforts on breast cancer screening have shown promising results for early intervention, localizing breast lesions has remained a challenge. This is because detection of breast lesions on mammogram images heavily depended on the radiologist's skill ( 1 ), which proved to be time consuming, and at times lacked the accuracy and precision Thus, this factor poses a serious challenge onto rapid diagnosis process which in the case of breast cancer, late detection may prove terminal. Advancements and involvement of artificial intelligence (AI) in the healthcare sector have improved accuracy and assisted radiologists by minimizing the rates of false positives and false negatives during clinical diagnosis.…”

Section: Introductionmentioning

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

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

Application of machine learning and image processing for detection of breast cancer

Cited by 19 publications

References 4 publications

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

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

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

Contact Info

Product

Resources

About