RETRACTED ARTICLE: Classification of ultrasound breast cancer tumor images using neural learning and predicting the tumor growth rate

Rani, V. Mary Kiruba; Dhenakaran, S. S.

doi:10.1007/s11042-019-7487-6

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…On the other hand, Suresh et al [ 49 ] and Sapate et al [ 50 ] employ a fuzzy-based strategy to cluster all the pixels with similar features in order to detect all the zones that have differences. Other strategies involve the utilization of mathematical morphology [ 51 , 52 , 53 , 54 , 55 ], image contrast and intensity [ 56 , 57 ], geometrical features [ 58 , 59 ], correlation and convolution [ 60 , 61 ], non-linear filtering [ 62 , 63 ], texture features [ 64 ], deep learning [ 65 , 66 , 67 , 68 , 69 ], entropy [ 70 , 71 ], among other strategies. It is worth noticing that from the diversity of the employed strategies, some of them still require an initial guidance to detect the suspicious zones, either by manually selecting pixels inside of the zone or using the radiologist notes about the localization.…”

Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado

Cruz-Albarran

Toledano‐Ayala

et al. 2022

Cancers

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Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

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Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado

Cruz-Albarran

Toledano‐Ayala

et al. 2022

Cancers

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“…Other researchers like [33], [17], [25], and [19] introduced different approaches using SVM, LDA, and Modified Neural Network (MNN) achieving notable accuracies ranging from 75.94% to 97.80%. Additionally, methods by [14] using logistic regression, [4] utilizing XGBoost, and [10] employing morphological features showed promising results with accuracies around 89.40% to 94.0%.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for Breast Cancer Classification: SVM Outperforms XGBoost, CNN, RNN, and Others

Ghosh,

Chatterjee

2024

Preprint

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This study evaluates ten machine learning algorithms for classifying breast cancer cases as malignant or benign based on physical attributes. Algorithms tested include XGBoost, CNN, RNN, AdaBoost, Adaptive Decision Learner, fLSTM, GRU, Random Forest, SVM, and Logistic Regression. Using a robust dataset from UCI machine learning Breast Cancer, SVM emerged as the most accurate, achieving 98.2456% accuracy. While AdaBoost, Logistic Regression, Neural Networks, and Random Forest showed promise, none matched SVM accuracy. These findings underscore the potential of machine learning, particularly SVMs, in cancer diagnosis and treatment by analyzing physical attributes for improved diagnostics and targeted therapies.

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“…Many noise reduction techniques have been developed that preserve the important details in the ultrasound image [ 19 , 20 ]. The filters working in the spatial domain are applied directly in the spatial image area.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

et al. 2022

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Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.

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RETRACTED ARTICLE: Classification of ultrasound breast cancer tumor images using neural learning and predicting the tumor growth rate

Cited by 10 publications

References 17 publications

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Comparative Analysis of Machine Learning Algorithms for Breast Cancer Classification: SVM Outperforms XGBoost, CNN, RNN, and Others

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

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