Mammogram classification using AdaBoost with RBFSVM and Hybrid KNN–RBFSVM as base estimator by adaptively adjusting γ and C value

Bhosle, Udhav; Deshmukh, Jyoti

doi:10.1007/s41870-018-0241-x

Cited by 12 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to accelerate the speed of face detection, firstly, the image is reduced in size according to the given scale. Face detection is implemented by the AdaBoost algorithm [22][23][24], which involves Haar features. AdaBoost is extracted based on gray scale image, which has certain requirements for image color.…”

Section: Image Preprocessing and Face Detectionmentioning

confidence: 99%

Evaluation Technology of Classroom Students’ Learning State Based on Deep Learning

Chen

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Facial features are an effective representation of students’ fatigue state, and the eye is more closely related to fatigue state. However, there are three main problems in the existing research: (1) the positioning of the eye is vulnerable to the external environment; (2) the ocular features need to be artificially defined and extracted for state judgment; and (3) although the student fatigue state detection based on convolutional neural network has a high accuracy, it is difficult to apply in the terminal side in real time. In view of the above problems, a method of student fatigue state judgment is proposed which combines face detection and lightweight depth learning technology. First, the AdaBoost algorithm is used to detect the human face from the input images, and the images marked with human face regions are saved to the local folder, which is used as the sample dataset of the open-close judgment part. Second, a novel reconstructed pyramid structure is proposed to improve the MobileNetV2-SSD to improve the accuracy of target detection. Then, the feature enhancement suppression mechanism based on SE-Net module is introduced to effectively improve the feature expression ability. The final experimental results show that, compared with the current commonly used target detection network, the proposed method has better classification ability for eye state and is improved in real-time performance and accuracy.

show abstract

Section: Image Preprocessing and Face Detectionmentioning

confidence: 99%

Evaluation Technology of Classroom Students’ Learning State Based on Deep Learning

Chen

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Bhosle and Deshmukh (2018) have proposed the Hybrid KNN-RBFSVM algorithm for breast cancer detection. At first, identical weights were allocated to every mammogram, and modernized weights were originated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yet, it is often very computationally expensive, and the dataset is avoided and the speed of the network is low. KNN + RBFSVM (Bhosle and Deshmukh, 2018) are very tough to noisy training data, and if training data is high, then the resultant outcome will be effective. But, there are a few challenges such as its computational cost is extremely high as distance of each query need to be computed to all the training samples, and it has many key parameters needed to set exactly to attain the best classification outcome for any difficulty; the classification accuracy is affected due to inconsistent and irrelevant features of mammogram.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifier

Patil

Biradar²

2020

IJICC

View full text Add to dashboard Cite

PurposeBreast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approachBreast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.FindingsThe performance analysis was done for both segmentation and classification. From the analysis, the accuracy of the proposed IAP-CSA-based fuzzy was 41.9% improved than the fuzzy classifier, 2.80% improved than PSO, WOA, and CSA, and 2.32% improved than GWO-based fuzzy classifiers. Additionally, the accuracy of the developed IAP-CSA-fuzzy was 9.54% better than NN, 35.8% better than SVM, and 41.9% better than the existing fuzzy classifier. Hence, it is concluded that the implemented breast cancer detection model was efficient in determining the normal, benign and malignant images.Originality/valueThis paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm (IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images, and this is the first work that utilizes this method.

show abstract

“…A low parameter C value makes a smooth decision, while a high C goals to classify all training samples correctly. The function of the SVM decision for binary classification problems is defined as follows [27].…”

Section: Support Vector Machine Classificationmentioning

confidence: 99%

SVM Optimization Based on PSO and AdaBoost to Increasing Accuracy of CKD Diagnosis

Indriani

Muslim

2019

LKJITI

View full text Add to dashboard Cite

Classification is data mining techniques which used for the purposes of diagnosis in the medical field as measured by the high accuracy produced. The accuracy of classification algorithm is influenced by the use of features and dimensions in dataset. In this study, Chronic Kidney Disease (CKD) dataset was used where the data is one of the high dimension datasets. Support Vector Machine (SVM) algorithm is used because its ability to handle high-dimensional data. In the dataset, it consists of 24 attributes and 1 class which if all are used results accuracy of classification will be diminished. Method for selecting features with Particle Swarm Optimization (PSO) is applied to reduce redundant features and produce optimal features. In addition, ensemble AdaBoost also applied in this research to increase performance of entirety classification algorithm. The results showed that the optimization of SVM algorithm by using PSO as a selection and ensemble feature of AdaBoost with an average of selected features of 18 features could increase the accuracy of 36.20% to 99.50% in the diagnosis of CKD compared to the SVM algorithm without optimization only resulting in accuracy 63.30%. This research can be used as a reference for further research in focusing on the preprocessing stage.

show abstract

Mammogram classification using AdaBoost with RBFSVM and Hybrid KNN–RBFSVM as base estimator by adaptively adjusting γ and C value

Cited by 12 publications

References 10 publications

Evaluation Technology of Classroom Students’ Learning State Based on Deep Learning

Evaluation Technology of Classroom Students’ Learning State Based on Deep Learning

Improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifier

SVM Optimization Based on PSO and AdaBoost to Increasing Accuracy of CKD Diagnosis

Contact Info

Product

Resources

About