Lesion Detection in Breast Ultrasound Images Using a Machine Learning Approach and Genetic Optimization

Torres, Fabián; Escalante-Ramı́rez, Boris; Olveres, Jimena; Yen, Ping-Lang

doi:10.1007/978-3-030-31332-6_26

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Next, based on minimizing the cost function (or increasing the fitness function), these obtained sequences are divided to separate parts and then each part adds to another part to create a new chain of spots within the searching space until stopping criteria are reached. The task of dividing and then adding sequences is done by considering their participation probability [ 68 – 70 ]. In the present research, the cost function is computed based on the difference between the input image and the image attained by the region growing technique.…”

Section: Extended Growth Region Methodsmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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Section: Extended Growth Region Methodsmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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“…ML is popular because it is more efficient, timely, and less expensive than deep learning methods, and because it does not require powerful computing hardware, it can be deployed in low-and middle-income countries [56][57][58]. These ML have been used in various applications: CAD, image registration, image segmentation, image fusion, image search, and annotation developed [59,60].…”

Section: Applications Of Machine Learning Model-based Hpomentioning

confidence: 99%

An Optimized Framework for Breast Cancer Classification Using Machine Learning

Michael

et al. 2022

BioMed Research International

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Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.

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“…Several studies report automatic methods (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), where modeling the knowledge of BUS and oncology as prior constraints is needed. Based on the literature, ML-based methods stand out among the most popular.…”

Section: Lesion Detection and Segmentationmentioning

confidence: 99%

“…Image features used in these methods should be appropriately selected according to the application, and texture information seems highly suitable for ultrasound images, but making an appropriate Feature Selection is an important step specially when dealing with high dimensionality spaces (30). Feature selection methods like Principal Component Analysis and Genetic algorithms have been used to improve the results in the detection and segmentation of lesions, with true positive fraction (TPF) values of 84.48%; however, these approaches still rely on manually designated features, which depend on good understanding of the A B images and the lesions (38). DL, which is a new field of ML, can directly learn abstract levels of features directly from images, solving the need to propose an initial set of features to describe the problem.…”

Section: Lesion Detection and Segmentationmentioning

confidence: 99%

What is new in computer vision and artificial intelligence in medical image analysis applications

Olveres¹,

González²,

Torres³

et al. 2021

Quant Imaging Med Surg

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Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.

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Lesion Detection in Breast Ultrasound Images Using a Machine Learning Approach and Genetic Optimization

Cited by 6 publications

References 24 publications

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

An Optimized Framework for Breast Cancer Classification Using Machine Learning

What is new in computer vision and artificial intelligence in medical image analysis applications

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