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

Patil, Rajeshwari S.; Biradar, Nagashettappa

doi:10.1108/ijicc-10-2019-0116

Cited by 20 publications

(6 citation statements)

References 35 publications

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“…The Genetic Algorithm (GA) disposes pointless information properties but doesn't give data that the framework can quickly utilize [16]. In [17][18][19] proposed a method called GA-moon to recognize breast cancer. The genetic algorithm is utilized for choosing the ideal properties from the general qualities.…”

Section: Literature Surveymentioning

confidence: 99%

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

Mishra¹,

Prakash²

2022

Computer Systems Science and Engineering

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Section: Literature Surveymentioning

confidence: 99%

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

Mishra¹,

Prakash²

2022

Computer Systems Science and Engineering

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“…Region growing (Patil and Biradar, 2020): The region growing-based segmentation is carried out by taking the obtained optimal cluster of FCM. In region growing, the threshold is optimized by the proposed M-DHOA to provide good segmentation results.…”

Section: Image Pre-processing and Improved Lung Segmentation For Covi...mentioning

confidence: 99%

Adaptive deep learning for deep COVID-19 diagnosis

S.¹,

M.K.²

2022

JEDT

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Purpose COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model. Design/methodology/approach The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets. Findings From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques. Originality/value This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

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“…Various algorithms, such as local and global thresholding based on image histograms are used to segment the breast masses [24]. In addition, methods based on regions, as an example, in the region growing, a seed point is utilized and the region is grown until it meets homogeneity criteria [22].…”

Section: Roi Segmentationmentioning

confidence: 99%

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

Abdulla

Sagheer

Veisi

2021

mendel

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Computer-aided diagnosis methods are being developed to assist radiologists to improve the interpretation of mammograms for the detection and diagnose of breast cancer, reduce the errors and mistakes made by human beings. In addition, it provides a more accurate and reliable classification of benign and malignant abnormalities. In the mammogram diagnosis, the pectoral muscle appears in Mediolateral oblique views (MLO) of the right and left of the breast. Considering that, the pectoral muscle has the same density as the small suspicious masses in the image and can affect/bias the results of image processing methods. This paper presents a diagnosis method to detect an abnormality in mammograms automatically. Before abnormality identification, image-processing techniques are used to correctly segment the suspicious region-of-interest (ROI). The background of the mammograms has been darkened to distinguish the breast area from any blemishes or writings that will be removed. Then the breast area has been extracted after ignoring the empty regions around the breast in mammogram images. After that, the mammogram image is inverted and the inverted image is then subtracted from the initial image. For pectoral muscle removal, a region growing method using the K-means clustering method is used. Afterward, suspicious ROI is segmented utilizing the K-means with thresholding technique. To detect abnormalities in mammograms, shape-based features, moment invariants, and also fractal dimensions are extracted from the segmented ROI. The Mini-MIAS dataset is used to evaluate the proposed method and is predominately composed of benign samples with only a tiny percent of malignant samples. To accomplish far better classifier efficiency, the SMOTE algorithm is used to present new samples from the minority classes to get a balanced dataset. Random forest classifier utilized to classify the segmented region as benign and malignant. The experimental results obtained an accuracy of 97.1%, the sensitivity of 95.1%, and the achieved specificity is 98.5%.

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

Cited by 20 publications

References 35 publications

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

Adaptive deep learning for deep COVID-19 diagnosis

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

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