Early detection of breast cancer using mammography images and software engineering process

Croock, Muayad Sadik; Khuder, Saja Dhyaa; Korial, Ayad E.; Mahmood, Sahar Salman

doi:10.12928/telkomnika.v18i4.14718

Cited by 11 publications

(11 citation statements)

References 16 publications

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“…Most of the discussed techniques applied ultrasound Doppler concept to measure the blood velocity. Gaining a new modern technique, deep-learning model is also designed for image classification to detect the early stage of disease in medical images [26]. Several reports have also explained the tracking techniques used for other application than the heart diagnostics.…”

Section: Resultsmentioning

confidence: 99%

Validation test on 3d heart phantom for mitral valve leaflet tracking

Mahadi

Ibrahim

Shanta

et al. 2021

IJEECS

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<span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US"><span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Mitral valve movement is essential to be identified in order to monitor the abnormality of blood flow in right side of heart. The estimation and tracking of mitral valve has seldom been investigated since it required high temporal rate to scan the echocardiography images and it depends on the operator to capture the low-speckle and-noise images. This study presents the validation experiment performed on heart phantom made of t<span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">hermoplastic polyurethane (TPU) filament which the objective is to validate the previous <span style="font-family: 'Times New Roman',serif; font-size: 9pt; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">features tracking technique implemented in mitral valve locating in video frames using Kanade-Lucas-Tomasi (KLT) algorithm. The outcome was able to automatically detect the edge of mitral valve and thus in future, it manages to predict the flowing of blood pattern. An in-vitro experiment was conducted which involved a valve phantom scanning in water tank that connected to water pump. It was found in this study that the technique capable to detect and visualize the mitral valve up to 59 frames in 2.36 secondsby tracking the features of minimum eigenvalue within the selected region. It was also produced a good agreement of valve distance between the true value and the measured one, which achieved the minimum of 88% similarity. This yielded the validation of the proposed technique to track and visualize the mitral valves.

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Section: Resultsmentioning

confidence: 99%

Validation test on 3d heart phantom for mitral valve leaflet tracking

Mahadi

Ibrahim

Shanta

et al. 2021

IJEECS

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“…These results indicate that this model is superior to existing approaches, such as TF [16], SVM [16], DBN [18], and SVM [21]. The performance of the proposed model is further evaluated by the medical experts of Inmol Cancer Hospital, Lahore, Pakistan.…”

Section: ()mentioning

confidence: 88%

“…The proposed IPBCS-FH-DL model achieves an accuracy of 97.81% and a miss rate of 2.19%. The proposed IPBCS-FHDL model obtains 0.75%, 1.31%, 4.95%, and 5.81% more accurate predictions and minimized the miss rate by 0.75%, 1.31%, 4.95%, 5.81% and 0.75% compared to SVM [16], TF [16], DBN [18] and SVM [21], respectively. This accuracy is achieved because the proposed model does not use handcrafted features as other existing methods do.…”

Section: ()mentioning

confidence: 94%

“…For the evolving automation of the proposed technique, this system was used for having a second opinion by pathologists. Zijun Sha [21] designed an inclusive method for the mammographic detection of breast cancer. The median filter was used to remove noise from images in the preprocessing phase, and a CNN was applied to identify the cancerous region.…”

Section: Literature Reviewmentioning

confidence: 99%

“…c. Finally, we evaluate our proposed deep learning-based model with other existing and state-of-the-art methods, such as using deep belief networks [18], support vector machines [21], and type 1 fuzziness [16]. In addition, the IPBCS-DL model was evaluated on an image-based breast cancer dataset [27].…”

Section: Literature Reviewmentioning

confidence: 99%

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IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

et al. 2021

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Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.INDEX TERMS Internet of medical things, breast cancer prediction, deep learning, convolutional neural network.

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The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups

Khudhur¹,

Khudhur

2023

Measurement: Sensors

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Early detection of breast cancer using mammography images and software engineering process

Cited by 11 publications

References 16 publications

Validation test on 3d heart phantom for mitral valve leaflet tracking

Validation test on 3d heart phantom for mitral valve leaflet tracking

IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups

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