Classification of breast ultrasound images based on texture analysis

Rahmawaty, Made; Nugroho, Hanung Adi; Triyani, Yuli; Ardiyanto, Igi; Soesanti, Indah

doi:10.1109/ibiomed.2016.7869825

Cited by 18 publications

(16 citation statements)

References 13 publications

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“…to detect various kind of cancers and tumors. Some automatic detection systems [11,33,48,53] classify medical images based on the features extracted using traditional handcrafted techniques whereas, others [13,15,16,38,54] use DLMs to perform feature extraction and classification of medical images. Recently, it has been observed that researchers have proposed automatic detection systems [5,26,52,60] that use an amalgamation of both the techniques and these systems exhibit good performance as compared to their counter-parts.…”

Section: Related Workmentioning

confidence: 99%

Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm

Bansal

Gehlot

Singhal

et al. 2022

Multimed Tools Appl

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Osteosarcoma is one of the most common malignant bone tumors mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and it is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify medical images, which reduces the dependency on experts and leads to faster processing. In this paper, an automatic detection system: Integrated Features-Feature Selection Model for Classification (IF-FSM-C) to detect osteosarcoma from the high-resolution whole slide images (WSIs) is proposed. The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted (HC) feature extraction techniques and deep learning models (DLMs) namely EfficientNet-B0 and Xception. To further improve the performance of the proposed system, feature selection (FS) is performed. Here, two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V are proposed to perform FS. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed to compare the performance of proposed IF-FSM-C to the classifiers which use HC or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection. The best overall accuracy of 96.08% is obtained when integrated features extracted using HC techniques and Xception are used. The overall accuracy is enhanced to 99.54% after applying BAOA-S for FS. Further, the application of BAOA-S for FS reduces the number of features with the best model having only 188 features compared to 2118 features if no FS is applied.

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Section: Related Workmentioning

confidence: 99%

Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm

Bansal

Gehlot

Singhal

et al. 2022

Multimed Tools Appl

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“…However, it caused a blurred image especially at the edges of the nodule. Rahmawaty et al [13] also conducted a similar study by comparing median filters and median adaptive filter. Both filters can remove markers and Rectangular RoI with breast nodules to be analysed is manually selected by the radiologists.…”

Section: Results and Analysismentioning

confidence: 97%

“…Morphological is an image processing method which modifies the spatial form or structure of objects involves the logical operation. The essence of the morphological method involves two-pixel arrays such as an image (A) and a kernel structure (B) [13]. The basic operations of morphological aredilation and erosion.…”

Section: Segmentationmentioning

confidence: 99%

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

et al. 2017

Self Cite

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“…This research focuses on the classification of breast cancer using USG images. Ultrasonography is one of the imaging modalities that can detect and classify breast mass abnormalities [7]. USG can be applied to examine the body by utilizing ultrasonic waves to get image of body tissue which in this case: breast tissue.…”

Section: Introductionmentioning

confidence: 99%

“…USG is considered cheaper, portable, and easier to find in health facilities than MRI [8]. But ultrasonographic images are very susceptible to noise speckle that appears at the time of acquisition or degradation of unclear edge characters [7], [8]. However, this matter depends on the operator at the time of examination, such as the examination techniques and module characteristics applied.…”

Section: Introductionmentioning

confidence: 99%

Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

Djunaidi¹,

Agtriadi²,

Kuswardani³

et al. 2021

IJEECS

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One way to detect breast cancer is using the Ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a <em>histogram</em> which then the results are calculated using the Gray Level Co-Occurrence Matrix (GLCM). The results of the two extraction features are then classified using K-Nearest Neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.

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Classification of breast ultrasound images based on texture analysis

Cited by 18 publications

References 13 publications

Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm

Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

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