Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach

Shiji, T P; Remya, S; Lakshmanan, Rekha; Pratab, Thara; Thomas, Vinu

doi:10.3233/jifs-179709

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…One of the key findings is that, methods that form ensemble of simpler classifiers, are able to outperform individual models significantly. Apart from these models, another classification model that has performed well is the SVC model, which is another model that has shown good results in many of the previous works (Asuntha et al, 2016; Maglogiannis et al, 2009; Shiji et al, 2020). Another key finding is that, handling of the class imbalance problem is very important in the medical image classification problem, which can be dealt with by using SMOTE based oversampling approach.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

et al. 2021

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Prediction of breast tumour malignancy using ultrasound imaging, is an important step for early detection of breast cancer. An efficient prediction system can be a great help to improve the survival chances of the involved patients. In this work, a machine learning (ML)—radiomics based classification pipeline is proposed, to perform this predictive modelling task, in a much more efficient manner. Multiple different types of image features of the region of interests are considered in this work, followed by a recursive feature elimination based feature selection step. Furthermore, a synthetic minority oversampling technique based step is also included in the pipeline, to deal with the class imbalance problem, that is often present in medical imaging datasets. Various ML models are considered in the subsequent model training phase, on a publicly available breast ultrasound image dataset (BUSI). From experimental analysis it has been observed that, shape, texture and histogram oriented gradients related features are the most informative, with respect to the predictive modelling task. Furthermore, it was observed that ensemble learners such as random forest, gradient boosting and AdaBoost classifiers are able to achieve significant results with respect to multiple evaluation metrics. The proposed approach achieved the state‐of‐the‐art accuracy, area under the curve, F1‐score and Mathews correlation coefficient values of 0.974, 0.97, 0.94 and 0.959, respectively, on the BUSI dataset. Such kind of impressive results suggest that the proposed approach can have a very high practical utility, in real medical diagnostic settings.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

et al. 2021

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“…Recently, new image generation techniques have developed, such as infrared thermography (IRT). This technique has been successfully applied to breast cancer; the classification methods included several machine learning and artificial neural networks, and the accuracy ranged from 90% to 100% [ 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 ]. Recently, new classification algorithms have been developed, including autoencoders, deep belief networks, ladder networks, and deep neural network (DNN)-based algorithms such as the deep Kronecker neural network [ 90 , 102 ].…”

Section: Table A1mentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Carreras

Roncador

Hamoudi

2022

Cancers

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Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

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“…After the suspicious zones are detected and segmented, it is necessary to extract features from them to generate the necessary information to classify the detected lesions as cancer or benign. To this purpose, Fourier Transform-based methods [ 48 , 72 ], wavelet transform-based strategies [ 73 , 74 , 75 , 76 ], geometric features [ 77 , 78 ], information theory algorithms [ 79 ], co-occurrence matrix features [ 47 , 80 , 81 , 82 ], histogram-based values [ 46 , 83 , 84 , 85 ], morphology [ 86 , 87 ], among others. On the other hand, with the increased capabilities (the number of simultaneous operations that can be done) of the new-generation graphical processor units, it is now possible to execute high-load computational algorithms faster than in a multicore processor [ 88 ]; in consequence, novel neural networks algorithms that perform the feature extraction and quantification are now being proposed.…”

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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Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach

Cited by 6 publications

References 30 publications

Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

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

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