A Novel Hybrid Perceptron Neural Network Algorithm for Classifying Breast MRI Tumors

Elnawasany, Amal; Ali, Ahmed Fouad; Waheed, Mohamed

doi:10.1007/978-3-319-13461-1_34

Cited by 4 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is again a supervised Machine Learning algorithm and a mathematical model which aids in integrating neurons so that messages are passed easily [33]. The three vital components present here such as input, output and transfer functions.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Prabhakar

Rajaguru²,

Lee

2019

IEEE Access

View full text Add to dashboard Cite

One of the most useful methodologies which provide a specific waveform showing the pulsating peripheral blood flow in a non-invasive manner is Photoplethysmography (PPG). The design, application and implementation of a PPG system are quite inexpensive and have a very easy maintenance. Without having direct contact with the surface of the skin, PPG can easily take the measurements. Therefore, PPG has a good medical competency and due to its widespread availability, it has a lot of advantages. A PPG signal can sometimes be substituted or complemented by an Electrocardiography (ECG) signal as it can provide Heart Rate Variability (HRV) analysis. In this work, an in-depth analysis of classification of Cardiovascular Disease (CVD) is done with the help of Capnobase dataset. Initially, metaheuristic optimization algorithms are utilized as dimensionality reduction techniques and then the dimensionally reduced values are classified with the help of different classifiers for the classification of CVD. The results show that for the PPG normal cases, a high classification accuracy of 99.48% is obtained when Chi square Probability Density Function (PDF) optimized values are classified with Artificial Neural Networks (ANN) and a second highest classification accuracy of 98.96% is obtained when Chicken swarm optimized values are classified with Naïve Bayesian Classifier (NBC). Similarly when the PPG abnormal cases or PPG with CVD cases are concerned, a high classification accuracy of 99.48% is obtained when Chi square PDF optimized values are classified with Logistic Regression and a second highest classification accuracy of 98.96% is obtained when Chi square CDF optimized values are classified with Gaussian Support Vector Machine (SVM) and when Chicken swarm optimized values are classified with NBC.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Prabhakar

Rajaguru²,

Lee

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Hassanien et al [ 38 ] introduced a hybrid model consisting of Pulse Couple Neural Network (PCNN) and Support Vector Machines (SVM) to identify breast cancer from MR images. Another hybrid algorithm was presented by ElNawasany et al in 2014 by combining perceptron with the Scale Invariant Feature Transform (SIFT) for the same purpose [ 42 ].…”

Section: Applications Of Annsmentioning

confidence: 99%

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

Mehdy

Shair

et al. 2017

Computational and Mathematical Methods in Medicine

125

View full text Add to dashboard Cite

Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.

show abstract

Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

et al. 2023

View full text Add to dashboard Cite

A Novel Hybrid Perceptron Neural Network Algorithm for Classifying Breast MRI Tumors

Cited by 4 publications

References 14 publications

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

Contact Info

Product

Resources

About