Breast thermal images classification using optimal feature selectors and classifiers

Lashkari, AmirEhsan; Pak, Fatemeh; Firouzmand, Mohammad

doi:10.1049/joe.2016.0060

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…SMO provides an easy way out for this problem. It divides the large problem into smaller ones and solves them separately with less memory requirement [35]. SMO can handle a large set of data as memory requirement is linear, not quadratic.…”

Section: Classification Algorithms: This Is a Binary Class Classificamentioning

confidence: 99%

Predicting anxiety and depression in elderly patients using machine learning technology

Bhakta

2017

Healthc. technol. lett.

108

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Anxiety and depression are two important mental health problems among the geriatric population. They are often undiagnosed and directly or indirectly responsible for various morbidities. Early and timely diagnosis has immense effect on appropriate management of anxiety and depression along with its co‐morbidities. Owing to time constraint and enormous patient load, especially in developing county such as India it is hardly possible for a physician or surgeon to identify a geriatric patient suffering from anxiety and depression using any psychometric analysis tool. So, it is of utmost importance to develop a predictive model for automated diagnosis of anxiety and depression among them. This Letter aims to develop an appropriate predictive model, to diagnose anxiety and depression among older patient from socio‐demographic and health‐related factors, using machine learning technology. Ten classifiers were evaluated with a data set of 510 geriatric patients and tested with ten‐fold cross‐validation method. Highest prediction accuracy of 89% was obtained with random forest (RF) classifier. This RF model was tested with another data set from separate 110 older patients for its external validity. Its predictive accuracy was found to be 91% and false positive (FP) rate was 10%, compared with gold standard tool.

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Section: Classification Algorithms: This Is a Binary Class Classificamentioning

confidence: 99%

Predicting anxiety and depression in elderly patients using machine learning technology

Bhakta

2017

Healthc. technol. lett.

108

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“…The study indicates that inclusion of higher order GLCM features do contribute in improving the classification percentage. AmirEhsan Lashkari et al 32 presented a fully automated technique to help physicians in early detection of breast cancer. It starts with finding Region of Interest (ROI) to improve the quality of image.…”

Section: Survey On Various Classification Methodsmentioning

confidence: 99%

The Effect of Thermography on Breast Cancer Detection-A Survey

Pavithra¹,

Ks²,

Sekar³

et al. 2018

SRP

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Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. Cancer has generally an abnormal cell that grows and invade healthy cells in the body. Breast cancer occurs when malignant tumors develop in the breast. Early detection of the breast cancer can effective in increasing the rate of survival. There are several Medical image techniques which has been used for detecting breast cancer. The survey explored the needs for the thermography over other screening tools. Thermography detects and records temperature changes on the surface of the skin which is known as Digital Infrared Thermal Imaging (DITI). Thermography is known for its features like non-invasive, painless, cost effective and high chances of recovery, which has got ability to detect the breast cancer at near beginning. There is no such tool which provides accurate predictability unless biopsy. But the combination of tools with thermography may boost the sensitivity and specificity when compared with other mechanisms.

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“…Many Researchers have extracted features from the images of the left and right breast for the detection of cancer. A. Lashkari et al [10] have used imaging technique based on thermography for diagnosing breast cancer based on degrees. The features extracted and selected were classified using supervised learning algorithms like SVM, k-NN, AdaBoost probability neural network and Naïve Bayes.…”

Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Diagnosis (BCD) Model Using Machine Learning

Israni¹

2019

IJITEE

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In the recent years, breast cancer research has made a significant growth however there is still a scope of advancement. Breast cancer increases the statistics of mortality among women. In concern to this issue, treatment of cancer should be started at the earlier stage, to increase the chances of survival of the patient. Thus, there is a need to diagnose breast cancer at the early stage using the features from the mammograms. This paper proposes an efficient BCD model to detect breast cancer by using Support Vector Machine (SVM) with 10-fold cross validation. The complexity of the problem increases if there are many input features for the diagnosis of cancer. Thus, Principal Component Analysis (PCA) is used to reduce the feature space from a higher dimension to a lower dimension. Experiment result shows that the PCA increases the accuracy of the model. The proposed BCD model is compared with other supervised learning algorithms like Decision trees (DT), Random Forest, k- Nearest Neighbors(k-NN), Stochastic Gradient Descent (SGD), AdaBoost, Neural Network (NN), and Naïve Bayes. Evaluation parameters like F1 measure, ROC curve, Accuracy, Lift curve and Calibration Plot proves that proposed BCD model outperforms and gives the highest accuracy among other compared algorithms

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Breast thermal images classification using optimal feature selectors and classifiers

Cited by 14 publications

References 26 publications

Predicting anxiety and depression in elderly patients using machine learning technology

Predicting anxiety and depression in elderly patients using machine learning technology

The Effect of Thermography on Breast Cancer Detection-A Survey

Breast Cancer Diagnosis (BCD) Model Using Machine Learning

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