Tumour Detection in Double Threshold Segmented Mammograms Using Optimized GLCM Features fed SVM

Unni, Ashwanth; Eg, Nidheep; Vinod, Saurav; Nair, Lekha S.

doi:10.1109/icacci.2018.8554738

Cited by 20 publications

(4 citation statements)

References 13 publications

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“…The equation used to find the feature value of an image using the GLCM method can be seen using equations 5, 6, 7, and 8 below: [18]. Where: P is the pixel value of an image, i is the row position of a pixel, j is the column position of a pixel, μ is the mean value, and σ is the variance value.…”

Section: Methodsmentioning

confidence: 99%

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Saleh

Laia

Gowasa

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.

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Section: Methodsmentioning

confidence: 99%

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Saleh

Laia

Gowasa

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Experimental results show that the accuracy obtained from this system is 97.66%, and the sensitivity is slightly less than 0.98. e deep network designed in this study is for breast cancer datasets only. Unni et al [21] used a general thresholding method to estimate the basal chest muscle boundary and then applied morphological methods to correct the extracted area boundaries and the mean filter to eliminate noise. e GLCM algorithm is 2…”

Section: Literature Reviewmentioning

confidence: 99%

Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network

Isfahani

Jannat-Dastjerdi

Eskandari

et al. 2021

Computational Intelligence and Neuroscience

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Mammography is a significant screening test for early detection of breast cancer, which increases the patient’s chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.

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“…The principal objective of preprocessing is to process an image such that the results are more suitable than the original image for a specific application [44]. Once the segmentation has been performed, the ROI is used to extract features, which can be extracted using GLCM features from the image by constructing the grey-level cooccurrence matrix of the image [45,46] 2 BioMed Research International (iii) Classification (accuracy of breast mass): contour segmentation plays an important role in CADx systems for mass classification [47], and an image segmented can be classified as normal, benign, and malignant [3] (iv) Treatment: the dose for breast cancer treatment depends on the size of tumour, which is an output of mammogram segmentation. Thus, every patient will have a different dose size and different treatment mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Michael

et al. 2021

BioMed Research International

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Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

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Tumour Detection in Double Threshold Segmented Mammograms Using Optimized GLCM Features fed SVM

Cited by 20 publications

References 13 publications

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network

Breast Cancer Segmentation Methods: Current Status and Future Potentials

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