Breast Cancer Diagnosis Using a Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

Alzubaidi, Abeer; Cosma, Georgina; Brown, David J.; Pockley, A. Graham

doi:10.1109/itag.2016.18

Cited by 25 publications

(14 citation statements)

References 23 publications

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“…[ 40 ]. GA has been used in several types of optimization problems due to its straightforwardness and robustness [ 41 , 42 , 43 ]. The GA implementation used in the experiments performed in this work was based on the MATLAB ® Global Optimization ToolBox TM .…”

Section: Methodsmentioning

confidence: 99%

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Leon

Martinez-Vega

Fabelo

et al. 2020

JCM

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Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

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Section: Methodsmentioning

confidence: 99%

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Leon

Martinez-Vega

Fabelo

et al. 2020

JCM

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“…Alzubaidi et al [7] proposes a hybrid feature selection approach for diagnosing breast cancer which has a combination of Genetic Algorithm (GA) and Mutual Information (MI) to select the best cancer predictor combination. The features that were selected features inputted into a classifier for predicting the presence of breast cancer in a patient.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the accuracy of the diagnosis is essential and an urgent issue in medical science community. Diagnosing the disease early will bring about a better treatment of patients and aid crucial decision making by medical experts [7]. A number of approaches have been employed in detecting cancer in patients ranging from blood tests, X-ray, CT scan, biopsy to patient examinations [5].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Fuzzy Expert System for Diagnosing Breast Cancer

Okikiola

Mustapha

et al. 2019

JAMCS

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The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

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“…For a given set of high dimensional feature space it will find the subset of features which might be most appropriate for the data mixing task [4]. A feature selection procedure consists of four major steps as shown below [5]:…”

Section: Introductionmentioning

confidence: 99%

Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

Kouser¹,

Priyam²

2018

IJET

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One of the open problems of modern data mining is clustering high dimensional data. For this in the paper a new technique called GAHDClustering is proposed, which works in two steps. First a GA-based feature selection algorithm is designed to determine the optimal feature subset; an optimal feature subset is consisting of important features of the entire data set next, a K-means algorithm is applied using the optimal feature subset to find the clusters. On the other hand, traditional K-means algorithm is applied on the full dimensional feature space. Finally, the result of GA-HDClustering is compared with the traditional clustering algorithm. For comparison different validity matrices such as Sum of squared error (SSE), Within Group average distance (WGAD), Between group distance (BGD), Davies-Bouldin index(DBI), are used .The GA-HDClustering uses genetic algorithm for searching an effective feature subspace in a large feature space. This large feature space is made of all dimensions of the data set. The experiment performed on the standard data set revealed that the GA-HDClustering is superior to traditional clustering algorithm.

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Breast Cancer Diagnosis Using a Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

Cited by 25 publications

References 23 publications

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Design and Implementation of a Fuzzy Expert System for Diagnosing Breast Cancer

Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

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