Distinguishing and clustering breast cancer according to hierarchical structures based on chaotic multispecies particle swarm optimization

Yassi, Maryam; Yassi, Alireza; Yaghoobi, Mehdi

doi:10.1109/iraniancis.2014.6802524

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Sinusoidal chaotic map acquired 99 percent precision from those maps because it matched with the position of the issue. The model achieves more than 90% precision [42].…”

Section: Description Of Existing Techniques For Breast Cancer Predictionmentioning

confidence: 98%

“…A fuzzy model was created by Yassi et al [42] to differentiate between normal and malicious breast cancer. The technique brought disorder into the hierarchical cluster of partial swarm enhancement of multispecies, prompting the improvement of chaotic hierarchical cluster-based multispecies swarm enhancement of particles (CHCMSPSE).…”

Section: Description Of Existing Techniques For Breast Cancer Predictionmentioning

confidence: 99%

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A comprehensive study on disease risk predictions in machine learning

Saranya¹,

Pravin²

2020

IJECE

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Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted.

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“…Sinusoidal chaotic map acquired 99 percent precision from those maps because it matched with the position of the issue. The model achieves more than 90% precision [42].…”

Section: Description Of Existing Techniques For Breast Cancer Predictionmentioning

confidence: 98%

Section: Description Of Existing Techniques For Breast Cancer Predictionmentioning

confidence: 99%

A comprehensive study on disease risk predictions in machine learning

Saranya¹,

Pravin²

2020

IJECE

View full text Add to dashboard Cite

show abstract

“…In 2014, Yassi et al [18] eleven formless maps are worn in the intelligent diagnosis system. The loosely precision recognize of distinct between affectionate and interdict censer is above 90 percent.…”

Section: Related Workmentioning

confidence: 99%

Mining based Optimization for Breast Cancer Analysis: A Review

Kumar¹,

Tomar²,

Gaur³

2015

IJCA

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World is regular to eyewitness a change in deaths apropos because of different cancer diseases. Ancient age ascertaining may count the ruin due to the cancer. In this paper we are analyzing the way for Cancer Classification and prediction so that we can prevent it in the earlier stages. For this we have studied several methodologies presented till now and based on the study presented earlier suggesting some future insights which may be a better framework for diagnosis. Risk Factors, Prevention and early detection are also discussed here. Our study focuses on mining and optimization techniques to find the remedies.

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“…Over the past decades, a large number of machine learning tools have been proposed, developed, and applied for this task. Yassi et al [2] proposed a Fuzzy system combined with optimization for distinguishing breast cancer types in 2014. Amrane et al [3] compared Naive Bayes and k-nearest neighbor classifiers in 2018, with KNN achieving 97.51% accuracy.…”

Section: Introductionmentioning

confidence: 99%

On the Wavelet Convolution Neural Networks for Ultra-Sound Based Breast Cancer Detection

Sriwichai,

Simtrakankul,

Onjun

et al. 2023

Fuzzy Systems and Data Mining IX

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Breast cancer is one of the deadliest types of cancer, and it comes in a wide variety of forms, resulting in a wide variety of detection methods. Several deep learning techniques have been applied to decrease unnecessary biopsies and lessen the burden on radiologists. One of the most popular architectures for this task is the Convolutional Neural Networks (CNNs). This paper aims to explore the integration of convolutional neural networks (CNN) and wavelet transform (WT) to identify the optimal combination and architecture of these methods for efficient detection of breast cancer in ultrasound images. To accomplish this task, the wavelet convolutional neural network (WCNN) structures are proposed and trained for the mission of screening breast cancer abnormalities embedded in ultrasound type of images. Compared with other two popular networks, ResNet50 and MobileNetV2, it has been found that the proposed WCNN has produced a satisfactory solution, with an accuracy of 98.24%, precision of 97.29%, recall of 100%, and F measure of 98.24%.

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Distinguishing and clustering breast cancer according to hierarchical structures based on chaotic multispecies particle swarm optimization

Cited by 4 publications

References 16 publications

A comprehensive study on disease risk predictions in machine learning

A comprehensive study on disease risk predictions in machine learning

Mining based Optimization for Breast Cancer Analysis: A Review

On the Wavelet Convolution Neural Networks for Ultra-Sound Based Breast Cancer Detection

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