Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors

Wisudawati, L.M.; Madenda, Sarifuddin; Wibowo, Eri Prasetyo; Abdullah, Arman Adel

doi:10.5539/mas.v14n5p51

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…After setting the orientation, we configured the number of graycomatrix to scale the image with the number of gray levels parameter and scales the values of graycomatrix with the gray limits parameter. In this study, the grayscale matrix using the number of gray levels was 32, which means 25 or 5 bits, and it uses the minimum and maximum grayscale values in the input image as constraints (17).…”

Section: Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Azimi Nanvaee,

Setayeshi

2023

Hepat Mon

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Background: Liver cancer is one of the most common types of cancer, in which early detection plays a significant role in preventing progression and reducing mortality. Ultrasound is one of the methods of liver examination recommended by guidelines due to its performance in detecting focal liver lesions. These small lesions may be missed in the early stages or diagnosed only when the prognosis is poor. Objectives: This study aimed to implement the best classification model for two liver stages by extracting optimal feature subsets to be used in computer-aided diagnosis systems (CAD). Methods: The model classifies the liver into two stages using B-mode ultrasound images of the liver. It involves extracting statistical texture features utilizing discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM). This study applied two feature selection methods: t-test and sequential forward floating selection (SFFS). The subset of selected features was presented to the k-nearest neighbor classifier for incorporation into a CAD system. Results: The accuracy, sensitivity, and specificity of the k-NN classifier were 98.75%, 98.82%, and 99.1%, respectively. Conclusions: Image analysis approaches were successfully performed to extract and select useful features. Therefore, this model is recommended for classifying two liver stages, normal and HCC.

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Section: Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Azimi Nanvaee,

Setayeshi

2023

Hepat Mon

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“…The method used in this stage is the median filter, thresholding, and mathematical morphology. This stage has been done before in research (Wisudawati et al, 2020) [14].…”

Section: Preprocessing Stagementioning

confidence: 99%

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Wisudawati¹,

Madenda²,

Wibowo³

et al. 2021

Computer Optics

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Breast cancer is a leading cause of death in women due to cancer. According to WHO in 2018, it is estimated that 627.000 women died from breast cancer, that is approximately 15 % of all cancer deaths among women [3]. Early detection is a very important factor to reduce mortality by 25-30 %. Mammography is the most commonly used technique in detecting breast cancer using a low-dose X-ray system in the examination of breast tissue that can reduce false positives. A Computer-Aided Detection (CAD) system has been developed to effectively assist radiologists in detecting masses on mammograms that indicate the presence of breast tumors. The type of abnormality in mammogram images can be seen from the presence of microcalcifications and the presence of mass lesions. In this research, a new approach was developed to improve the performance of CAD System for classifying benign and malignant tumors. Areas suspected of being masses (RoI) in mammogram images were detected using an adaptive thresholding method and mathematical morphological operations. Wavelet decomposition is performed on the Region of Interest (RoI) and the feature extraction process is performed using a GLCM method with 4 statistical features, namely, contrast, correlation, entropy, and homogeneity. Classification of benign and malignant tumors using the MIAS database provided an accuracy of 95.83 % with a sensitivity of 95.23 % and a specificity of 96.49 %. A comparison with other methods illustrates that the proposed method provides better performance.

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Breast cancer identification using feature level fusion and hybrid GA-PSO optimized neural network

Gupta

Kashyap

2022

2022 IEEE 6th Conference on Information and Communication Technology (CICT)

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Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors

Cited by 4 publications

References 13 publications

Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Breast cancer identification using feature level fusion and hybrid GA-PSO optimized neural network

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