A Hybrid Deep Learning Model for Breast Cancer Mammographic Image Classification Based on Transfer Learning and an Attention Module
| المصدر: | مجلة جامعة الزيتونة الدولية |
|---|---|
| الناشر: | جامعة الزيتونة الدولية |
| المؤلف الرئيسي: | Mousa, Tawfik Ezat (Author) |
| مؤلفين آخرين: | Geoda, Mohamed S. (Co-Author) |
| المجلد/العدد: | ع25 |
| محكمة: | نعم |
| الدولة: |
سوريا |
| التاريخ الميلادي: |
2024
|
| الشهر: | أغسطس |
| الصفحات: | 1 - 10 |
| ISSN: |
2958-8537 |
| رقم MD: | 1540533 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EduSearch |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Breast Cancer | VGG16 | MIAS | Mammography | Classification
|
| رابط المحتوى: |
عدد مرات التحميل
1
| المستخلص: |
Breast cancer is one of the primary causes of death among women. Early detection of breast cancer allows for the receipt of appropriate treatment, thus increasing the possibility of survival. In this paper, we proposed a hybrid deep learning model using a pre-trained VGG16 model with a self-attention mechanism for breast cancer detection. We extract features from the binary class (benign, malignant) dataset of the mammographic image analysis society (MIAS) using pre-trained deep convolutional neural network (CNN) architectures like Xception, MobileNet, DenseNet, and VGG-16. So the results illustrated that the best model is VGG16 with a self-attention module, which achieved an accuracy of 98.77%. |
|---|---|
| ISSN: |
2958-8537 |
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