Applying Convolutional Neural Networks For Image Detection

Karam, Abdel-Fatah; Embaby, Mohamed; El-Kady, Hazem; Abdel-Hafeez, Samir; Nabil, G.; Mohammed, Ammar

doi:10.1109/smartnets48225.2019.9069750

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Deep learning methods [21,22,23,24] have widely used in several type of application domains [25,26,27,28,29,30,31]. These techniques have a significant influence on the early diagnosis of a variety of diseases [32,33].…”

Section: Related Workmentioning

confidence: 99%

The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

Mohamed

Amer²,

Eldin

et al. 2022

Journal of Computing and Communication

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According to the World Health Organization, cancer is the second leading cause of mortality. Breast cancer is the most prevalent cancer diagnosed in women around the world. Breast cancer diagnostics range from mammograms to CT scans and ultrasounds, but a biopsy is the only way to know for sure if the suspicious cells detected in the breast are cancerous or not. This paper's main contribution is multi-fold. First, it proposes a deep learning approach to detect breast cancer from biopsy microscopy images. Deep convolution nets of various types are used. Second, the paper examines the effects of different data preprocessing techniques on the performance of deep learning models. Third, the paper introduces an ensemble method for aggregating the best models in order to improve performance. The experimental results revealed that Densenet169, Resnet50, and Resnet101 are the three best models achieving accuracy scores of 62%, 68%, and 85%, respectively. without data preprocessing. With the help of data augmentation and segmentation, the accuracy of these models increased by 20%, 17%, and 6%, respectively. Additionally, the ensemble learning technique improves the accuracy of the models even further. The results show that the best accuracy achieved is 92.5%.

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