The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM

Gohariyan, Elham; Esmaeilpour, Mansour; Shirmohammadi, Mohammad Mehdi

doi:10.9781/ijimai.2017.453

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Automatic detection of breast cancer has been considered by several researchers (Gohariyan et al, 2017; You & Rumbe, 2010). Although most of the previous research has focused on utilizing mammography imaging, recently, researchers have started considering the problem of breast tumour detection and classification in US images with the help of ML and DL‐based techniques.…”

Section: Related Workmentioning

confidence: 99%

A multi‐task learning based approach for efficient breast cancer detection and classification

et al. 2022

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Automatic segmentation and classification of breast tumours in ultrasound images using deep learning approaches can help early detect breast cancer. Such predictive modelling can potentially significantly improve the survival chances of the involved patients. Most of the typical deep convolutional neural network (CNN) based approaches consider segmentation and classification tasks separately. But this loses important supervisory information to help achieve better model training. This work proposes the integrated learning of both of these tasks in an end‐to‐end manner, using a multi‐task learning based approach. More specifically, a convolutional encoder‐decoder based architecture is coupled with a residual CNN for performing segmentation and classification together. The level‐wise feature maps from both the encoder and decoder parts of the segmentation network are utilized for classification in the proposed approach. From experimental analysis on a publicly available breast ultrasound image (BUSI) dataset, it has been observed that the proposed approach can achieve impressive performances, both with respect to tumour segmentation and classification. A mean test set AUC of 0.97 and a mean dice score of 0.74 is achieved, establishing a new state‐of‐the‐art performance on the BUSI dataset. From the impressive experimental observations, it can be concluded that learning to perform both segmentation and classification simultaneously can have a very high positive impact on the overall quality of the predictive model. Such observations suggest that the proposed approach can be beneficial in providing real‐time decision support to the involved diagnostic radiologists, which can help improve the survival chances of the corresponding patients.

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Section: Related Workmentioning

confidence: 99%

A multi‐task learning based approach for efficient breast cancer detection and classification

et al. 2022

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“…Max-min with the east variance method (Raj kumar & Raju, 2015) is applied to extract the abnormal part in breast images. Fuzzy C Means combined with a level set with PSO (Elham Gohariyan et al, 2016) is used to segment the MR brain images and proved its accuracy with limited images. Combination of Ant Bee colony-Extreme Learning Machine -Kernel Fuzzy C Means clustering (Hemalatha et al, 2018) is used for segmenting the tumor part of brain images.…”

Section: Related Workmentioning

confidence: 99%

Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

Sumathi¹,

Mandadi²

2021

International Journal of E-Health and Medical Communications

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The authors designed an automated framework to segment tumors with various image sequences like T1, T2, and post-processed MRI multimodal images. Contrast-limited adaptive histogram equalization method is used for preprocessing images to enhance the intensity level and view the tumor part clearly. With the combination of kernel possibilistic c means clustering with particle swarm optimization technique, a tumor part is segmented, and morphological filters are applied to remove the unrelated outlier pixels in the segmented image to detect the accurate tumor part. The authors collected various image sequences from online resources like Harvard brain dataset, BRATS, and RIDER, and a few from clinical datasets. Efficiency is ensured by computing various performance metrics like Jaccard Index MSE, PSNR, sensitivity, specificity, accuracy, and computational time. The proposed approach yields 97.06% segmentation accuracy and 98.08% classification accuracy for multimodal images with an average of 5s for all multimodal images.

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“…This type of tumor is the most prevalent in women around the world and early and accurate diagnosis is the key point for a successful treatment. The first article [3] analyzes the usefulness of neural networks and supported vector machines for the study of mammography and MRI (magnetic resonance) lesions. In the second one [4], the authors introduce a mathematical method to analyze radiologic-mammography contour of the lesions to distinguish between benign and malign pathologies.…”

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confidence: 99%

IJIMAI Editor's Note - Vol. 4 Issue 5

Carretero¹

2017

IJIMAI

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The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM

Cited by 4 publications

References 2 publications

A multi‐task learning based approach for efficient breast cancer detection and classification

A multi‐task learning based approach for efficient breast cancer detection and classification

Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

IJIMAI Editor's Note - Vol. 4 Issue 5

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