Extraction of Tumour in Breast MRI using Joint Thresholding and Segmentation – A Study

Kadry, Seifedine; Damaševičius, Robertas; Taniar, David; Rajinikanth, V.

doi:10.1109/icbsii51839.2021.9445152

Cited by 25 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are numerous breast cancer classification processes that are mentioned on literature review, 13,[26][27][28][29][30][31][32][33][34][35][36] which methods have some limitations, like low detection rate of benign samples likened with high fraction, the accuracy of Benign is obviously categorize and diminish that accuracy of malignant, some processes do not obviously categorize malignant and benign display the accuracy of normal region, certain approaches display the accuracy of image. In this process it overcomes all these problems and provides more precision.…”

Section: Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

In this manuscript, a Wrapper based feature extraction framework based on AlexNet deep convolutional neural network (ADCNN) with gradient‐based optimizer (GBO) is proposed for early detection of breast cancer. In this input images are occupied as mini‐mammography image analysis society (MIAS) database. Then the images are pre‐processed to eliminate that noises using Markov random field (MRF) method. Image features are extracted by the process of Wrapper based feature extraction framework with ADCNN. Then, the weight parameters of ADCNN are optimizing through the aid of GBO. Then the mammogram images are characterized as normal or abnormal (malignant and benign) with SVM classifier. The simulation process is implemented on MATLAB platform. The proposed ADCNN‐SVM‐GBO attains higher accuracy 34.64%, 28.86%, 19.86%, 24.64%, 32.86%, higher Precision 28.07%, 18.96%, 16.86%, 25.86%, 26.86%, higher recall 27.86%, 32.54%, 27.86%, 23.95%, 19.97%, and the efficiency of the proposed method FE‐ADCNN‐GBO‐SVM is likened with the existing processes. Classification of mammograms depends features removal processes with support vector machine (FE‐LBP‐GLCM‐SVM), breast cancer classification with global discriminate features on mammographic images (FE‐GLCM‐ANN), enhancing breast cancer classification with (SMOTE) technique and pectoral muscle removal on mammographic images (FE‐SMOTE‐RF), application of artificial intelligence depends deep learning on breast cancer screening and imaging diagnosis (FE‐CNN‐CDCNN) respectively.

show abstract

Section: Problem Statementmentioning

confidence: 99%

“…Kadry et al,31 have reported tumor removal on breast MRI using threshold and joint segmentation. In this, images of the breast cancer are taken as MRI slices.…”

mentioning

confidence: 99%

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…A custom 10-layer CNN was used to segment color skin lesion images, while the ResNet101 and DenseNet201 models were used for deep feature extraction, and an improved moth flame optimization algorithm was used for the selection of discriminative features [20]. A trilevel thresholding based on slime mould algorithm and Shannon's entropy was used to enhance the Breast-Tumor-Section of the slices of breast magnetic resonance imaging (MRI) images, while segment extraction was done using watershed segmentation and further applied for breast cancer recognition [21]. Slime mould optimization algorithm was used for extracting blood vessel from digital fundus images for the recognition of retinal disease [22].…”

Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

View full text Add to dashboard Cite

Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

show abstract

“…Consequently, U-Net is useful for image segmentation processes that need a moderate quantity of data, and it has shown high efficiency in working with medical images [9][10] and other applications. Kadry et al [11] conducted an investigation on brain stroke segmentation using a Visual Geometry Group UNet (VGG-UNet). It does, however, need a large number of nodes in the input layer due to the fact that it performs repeating convolution operations for feature engineering.…”

Section: Introductionmentioning

confidence: 99%

Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks

et al. 2022

View full text Add to dashboard Cite

Segmentation of brain regions affected by ischemic stroke helps to overcome the main obstacles in modern studies of stroke visualization. Unfortunately, contemporary methods of solving this problem using artificial intelligence methods are not optimal. Therefore, in the study we consider how to increase the efficiency of segmentation of the stroke focus using computer perfusion imaging using modifications based on UNet. The network was trained and tested using the ISLES 2018 dataset. The publication includes an analysis of the results obtained, as well as recommendations for future research. By choosing the appropriate model parameters, our approach can be easily applied to detect ischemic stroke. We present modified U-Net models with two ResNet blocks as U-Net+ ResNetblock 1 and U-Net +ResNetblock 2, as well as a modified UNet model. Due to the small number of images for training the model, the best results were obtained by applying data preprocessing and object representation approaches, as well as data normalization methods to avoid overfitting. The results show that the modified UNet model is superior to other models in terms of average distance and recall, that are significant parameters for segmentation of the stroke.

show abstract

Extraction of Tumour in Breast MRI using Joint Thresholding and Segmentation – A Study

Cited by 25 publications

References 17 publications

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks

Contact Info

Product

Resources

About