Breast Cancer Segmentation Based on GVF Snake by Mahfuzah, Mustafa, Nur Azwa, Omar Rashid, Rosdiyana, Samad, Norizam, Sulaiman

“…Breast cancer is a mysterious disease that forms through the formation of cancer in the cells of the breast. …”
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A UWB imaging system to detect early breast cancer in heterogeneous breast phantom by Saleh, Alshehri, Adznan, Jantan, R. S. A., Raja Abdullah, Rozi, Mahmud, Sabira, Khatun, Zaiki, Awang

“…This paper presents an experimental early breast cancer detection system in terms of heterogeneous breast phantom. …”
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A UWB imaging system to detect early breast cancer in heterogeneous breast phantom by Alshehri, Saleh Ali, Jantan, Adznan, Raja Abdullah, Raja Syamsul Azmir, Mahmud, Rozi, Khatun, Sabira, Awang, Zaiki

“…This paper presents an experimental early breast cancer detection system in terms of heterogeneous breast phantom. …”
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Performance verification on UWB antennas for breast cancer detection by V., Vijayasarveswari, M., Jusoh, T., Sabapathy, Rafikha, Aliana, Sabira, Khatun, Z. A., Ahmad, Mohamed Nasrun, Osman

“…Breast cancer is a common disease among women and death figure is continuing to increase. …”
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Stochastic Gompertzian model for breast cancer growth process by Mazma Syahidatul Ayuni, Mazlan, Norhayati, Rosli

“…In this paper, a stochastic Gompertzian model is developed to describe the growth process of a breast cancer by incorporating the noisy behavior into a deterministic Gompertzian model. …”
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Deep learning method based for breast cancer classification by Irmawati, Irmawati, Ernawan, Ferda, Fakhreldin, Mohammad Adam Ibrahim, Saryoko, Andi

“…The most prevalent cancer in women worldwide and one of the main factors in cancer-related mortality is breast cancer. …”
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Developing a religious program for breast cancer survivors by Alwi, Khadijah, Ahmad, Farizah, Amadhila, Helena Naitsuwe

Subjects: “…Breast - Cancer - Patients - Rehabilitation…”
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Synergy network based inference for breast cancer metastasis by Kabir Ahmad, Farzana, Deris, Safaai, Abdullah, Mohd. Syazwan

“…Breast cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. …”
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Breast cancer detection using modern visual IT techniques by Mambou, Sebastien, Maresova, Petra, Krejcar, Ondrej, Selamat, Ali, Kuca, Kamil

“…Nowadays, cancer is a major cause of women death, especially breast cancer which is most seen on ladies older than 40 years. …”

Curvelet based texture features for breast cancer classifications by Yasiran, Siti Salmah, Salleh, Shaharuddin, Sarmin, Norhaniza, Mahmud, Rozi, Abd. Halim, Suhaila

“…One of the sources of death among women is breast cancer. It is well known that Mammogram is the best method for breast cancer detection. …”
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Knowledge of breast cancer and breast self-examination practice among Yemeni female school teachers in Malaysia by Noman, Sarah Noman Abdulrahman, Abdul Rahman, Hejar, Akhtari-Zavare, Mehrnoosh, Al-Wajeeh, Nahla Saeed, Kadir @ Shahar, Hayati, Ismail, Suriani

“…Background: Breast cancer is the most frequent cancer and the second cause of cancer deaths among women worldwide, including Yemeni women. …”
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Nanomedicine using Quercus infectoria Extract for the treatment of Breast Cancer by Wan Nur, Suzilla, Mahmood, Syed, Rajaletchumy, Veloo Kutty

“…However, the information on the phytochemical content and the cytotoxicity against breast cancer cells are not well established. Therefore in this study, QI galls is chosen for its evaluation for phytochemical constituents and cytotoxic activity against breast cancer cells (MDA-MB-231 and MCF-7). …”
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Haralick texture and invariant moments features for breast cancer classification by Yasiran, Siti Salmah, Salleh, Shaharuddin, Mahmud, Rozi

“…Classification of breast cancer is essential in determining the type of treatment that should be applied. …”
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Breast cancer detection using forward scattering radar technique by Munawar, Aris, Adabi, Saba, Ismail, Alyani, Saripan, M. Iqbal, Mahmud, Rozi, Wan Mahadi, Wan Nor Liza, Raja Abdullah, Raja Syamsul Azmir

“…This paper presents an initial investigation for breast cancer detection using a special mode of bistatic radar system known as forward scattering radar (FSR). …”
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Haralick texture and invariant moments features for breast cancer classification by Yasiran, S. S., Salleh, S., Mahmud, R.

“…Classification of breast cancer is essential in determining the type of treatment that should be applied. …”

Application of deep learning method in facilitating the detection of breast cancer by Samah, Azurah A., Nasien, Dewi, Hashim, Haslina, Sahar, Julia, Abdul Majid, Hairudin, Yusoff, Yusliza, Ali Shah, Zuraini

“…Breast cancer is a type of tumour that could be treated if the disease is identified at an earlier stage. …”
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Comparison on some machine learning techniques in breast cancer classification by Mashudi, N. A., Rossli, S. A., Ahmad, N., Mohd. Noor, N.

“…Breast cancer is the second most common cancer after lung cancer and one of the main causes of death worldwide. …”
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Breast cancer prediction and detection: comparison of the latest machine learning techniques by Nya Yanga, Ornella Kelly, Homayounvala, Elaheh

“…Breast cancer is a prevalent and potentially deadly disease affecting women in the UK, with 1 in 7 women and 1 in 1,000 men at risk Early diagnosis, effective treatment, awareness, lifestyle choices, genetic testing, and research efforts have helped reduce mortality and improve patient outcomes. …”
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The Prognosis of Breast Cancer: A Comparison of Different Neural Network Architectures by Hamdan, H., Taib, N.M., Kareem, S.A., Har, Y.C.

“…This paper describes the research on the application of artificial neural networks in the prognosis of breast-cancer based on the cases seen in the University of Malaya Medical Centre from the year 1993 to 2002…”

Psychological distress among breast cancer survivors: a preliminary study by Hashim, Zailina, Yong, Heng Weay, Mohd Shariff, Zalilah

“…Many of the study had successfully revealed the association between genetic, lifestyle and nutritional factors with breast cancer. The survivor may also face with some of the psychological distress (e.g., stress, anxiety, fatigue and depression) in their lifetime. …”
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