A Hybrid CNN and RBF-Based SVM Approach for Breast Cancer Classification in Mammograms

Alkhaleefah, Mohammad; Wu, Chao-Cheng

doi:10.1109/smc.2018.00159

Cited by 37 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To implement, train and validate the proposed algorithm, we used Python (with all the necessary libraries) and Google colaboratory. A quick review of the state of the art in this context [9, 13,24] allows us to conclude that some researchers are only interested in the standard evaluation metrics of the algorithms they propose. They often avoid giving more information about the database used, the evaluation method and the associated risks of error.…”

Section: Iii-results and Discussionmentioning

confidence: 99%

Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis

et al. 2021

View full text Add to dashboard Cite

Breast cancer is a real public health problem in Morocco. It is the cause of a significant number of deaths caused by late diagnosis. Mammography plays an essential role in the detection of breast cancer and in the early management of its treatment. Despite the existence of screening programs, there are still high rates of false positives and false negatives. Indeed, women were called back for additional diagnoses based on suspicious results that eventually led to cancer. Artificial intelligence (AI) algorithms represent a promising solution to improve the accuracy of digital mammography offering, on the one hand, the possibility of better cancer detection, and, on the other hand, improved efficiency for radiologists for good decision-making. In this work, through a review of the literature on the tools used to evaluate the performance of AI systems dedicated to early detection and diagnosis of breast cancer. We set out to answer the following questions: Is the ethics relating to patient data during the development phase of this software is respected? Do these tools take into consideration the specificities of the field? What about the specification, accuracy and limitations of these applications? At the end, we show through this work recommendations to adapt these evaluation tools of AI applications for breast cancer screening for an optimized and rational consideration of the principle of health vigilance and compliance with the regulatory standards in force governing this field.

show abstract

Section: Iii-results and Discussionmentioning

confidence: 99%

Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, the best performance metrics observed were the prediction model of prematurity from medical images using the SVM technique with an accuracy of 95.7% and the prediction of neonatal mortality using the XGBoost technique with an accuracy of 99.7%. SVM has shown simplicity and flexibility to address several classification problems and also offers balanced predictive performance even in studies where sample sizes may be limited ( Alkhaleefah and Wu, 2018 ). The XGBoost technique is a very effective and widely used ML method that data scientists use to achieve state-of-the-art results in many ML challenges ( Wang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Bertini

Salas

Chabert

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.

show abstract

“…CNN consists of neurons arranged in a 1, 2, or 3-dimensional way (width, height, depth) which transform input values into neuron output class values activations via a series of hidden layers. The popular layers on CNN are CONV, POOL and FC with the input and output layers [41]. CNN models of the neural network may take long time for data set training.…”

Section: Figure3mentioning

confidence: 99%

Detection of COVID-19 in Computed Tomography (CT) Scan Images using Deep Learning

2020

IJATCSE

View full text Add to dashboard Cite

Coronavirus disease (COVID-19) is a fresh genus found in 2019 that was not previously known in humans. On the other hand, Deep learning is one of the most important fields of medical imaging science at present. In this article, a model of deep learning is being trained for the COVID-19 detection in CT Scan images.This study is implemented using Python programming language. To build and train the Convolution Neural Network (CNN) model, Python Deep Learning libraries such as Keras and TensorFlow 2.0 have been utilized. As for the dataset, the open source dataset of COVID-19 chest computed tomography (CT) images were used. These image where been confirmed by the senior radiologist who performed Diagnosis of and treatment of patients with COVID-19. There were total of 745 images belonging to two classes were sampled. 348 positive (+) COVID-19 images and 397 negative (-) COVID-19 images. Based on the training process, the model was able to detect 79 per cent accuracy on the test set. The performance of the model, Convolution Neural Network were evaluated by comparing with Logistic Regression model. Findings from the research proves that Convolution Neural Network are reliable by producing higher accuracy rate of 79% while Logistic Regression produce a rate of 54%. However, in the future more reliable and quality image datasets should be used along with the metadata of the patients to train the model.

show abstract

A Hybrid CNN and RBF-Based SVM Approach for Breast Cancer Classification in Mammograms

Cited by 37 publications

References 11 publications

Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis

Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Detection of COVID-19 in Computed Tomography (CT) Scan Images using Deep Learning

Contact Info

Product

Resources

About