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An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images
المؤلفون المشاركون
Guerrisi, M.
Aiello, Marco
Duggento, A.
Cavaliere, C.
Cascella, Giuseppe L.
Cascella, Davide
Conte, Giovanni
Toschi, Nicola
المصدر
Contrast Media & Molecular Imaging
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-05-22
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Breast cancer is one of the most common cancers in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide.
In this context, recent studies showed that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long term.
X-ray mammography is still the instrument of choice in breast cancer screening.
In this context, the false-positive and false-negative rates commonly achieved by radiologists are extremely arduous to estimate and control although some authors have estimated figures of up to 20% of total diagnoses or more.
The introduction of novel artificial intelligence (AI) technologies applied to the diagnosis and, possibly, prognosis of breast cancer could revolutionize the current status of the management of the breast cancer patient by assisting the radiologist in clinical image interpretation.
Lately, a breakthrough in the AI field has been brought about by the introduction of deep learning techniques in general and of convolutional neural networks in particular.
Such techniques require no a priori feature space definition from the operator and are able to achieve classification performances which can even surpass human experts.
In this paper, we design and validate an ad hoc CNN architecture specialized in breast lesion classification from imaging data only.
We explore a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy.
We achieve an area under the receiver operatic characteristics curve of 0.785 (accuracy 71.19%) on the test set, demonstrating how an ad hoc random initialization architecture can and should be fine tuned to a specific problem, especially in biomedical applications.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Duggento, A.& Aiello, Marco& Cavaliere, C.& Cascella, Giuseppe L.& Cascella, Davide& Conte, Giovanni…[et al.]. 2019. An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images. Contrast Media & Molecular Imaging،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1130304
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Duggento, A.…[et al.]. An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images. Contrast Media & Molecular Imaging No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1130304
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Duggento, A.& Aiello, Marco& Cavaliere, C.& Cascella, Giuseppe L.& Cascella, Davide& Conte, Giovanni…[et al.]. An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images. Contrast Media & Molecular Imaging. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1130304
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130304
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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