Self-regulated multilayer perceptron neural network for breast cancer classification

Ting, Fung Fung; Sim, K. S.

doi:10.1109/icoras.2017.8308074

Cited by 26 publications

(5 citation statements)

References 7 publications

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“…Amidst of various methodologies, multilayer perceptrons are broadly used methods for detecting cancer. ey are also widely used for predicting complex types of issues in cancer with higher rates in accuracy [27]. Similar work has been carried out by authors [28] that combines CNN-LSTM to detect COVID-19 using X-rays that automatically identify the diseases before they spread.…”

Section: Multilayer Perceptronmentioning

confidence: 90%

A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis

et al. 2022

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The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily.

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Section: Multilayer Perceptronmentioning

confidence: 90%

A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis

et al. 2022

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“…Each neuron weights the input nodes and generates the output by employing nonlinear activation mathematical functions. The linear combination is formed by perceptron through the computation of an output neuron from multiple real‐valued inputs [59]. The model was implemented using the Python package sklearn with the MLPClassifier function.…”

Section: Materials and Methodstcgamentioning

confidence: 99%

Performance‐weighted‐voting model: An ensemble machine learning method for cancer type classification using whole‐exome sequencing mutation

Luo

2020

Quant. Biol.

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BackgroundWith improvements in next‐generation DNA sequencing technology, lower cost is needed to collect genetic data. More machine learning techniques can be used to help with cancer analysis and diagnosis.MethodsWe developed an ensemble machine learning system named performance‐weighted‐voting model for cancer type classification in 6,249 samples across 14 cancer types. Our ensemble system consists of five weak classifiers (logistic regression, SVM, random forest, XGBoost and neural networks). We first used cross‐validation to get the predicted results for the five classifiers. The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions. The final predicted probability of the performance‐weighted‐voting model for a cancer type can be determined by the summation of each classifier’s weight multiplied by its predicted probability.ResultsUsing the somatic mutation count of each gene as the input feature, the overall accuracy of the performance‐weighted‐voting model reached 71.46%, which was significantly higher than the five weak classifiers and two other ensemble models: the hard‐voting model and the soft‐voting model. In addition, by analyzing the predictive pattern of the performance‐weighted‐voting model, we found that in most cancer types, higher tumor mutational burden can improve overall accuracy.ConclusionThis study has important clinical significance for identifying the origin of cancer, especially for those where the primary cannot be determined. In addition, our model presents a good strategy for using ensemble systems for cancer type classification.

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“…The development of intelligent algorithms plays a crucial role in achieving both accuracy and speed in the diagnostic process [ 1 – 3 ]. These algorithms provide medical experts with the necessary tools to enhance their diagnoses [ 4 , 5 ]. Currently, machine learning (ML) and predictive modelling techniques [ 6 , 7 ] are utilized for such tasks.…”

Section: Introductionmentioning

confidence: 99%

Medical image diagnosis based on adaptive Hybrid Quantum CNN

Ajlouni,

Özyavaş,

Takaoğlu

et al. 2023

BMC Med Imaging

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Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum–classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This is followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness is based on the development of an optimizer with a loss base adaptive momentum. Several algorithms are developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms are tested against a Kaggle brain dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.

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Self-regulated multilayer perceptron neural network for breast cancer classification

Cited by 26 publications

References 7 publications

A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis

A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis

Performance‐weighted‐voting model: An ensemble machine learning method for cancer type classification using whole‐exome sequencing mutation

Medical image diagnosis based on adaptive Hybrid Quantum CNN

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