# CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis
###### Abstract  
In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world's 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN models may become successfully fine-tuned to obtain improved findings. To conduct the automatic detection of BC by the CBIS-DDSM dataset, a CNN model, namely CoroNet, is proposed. It relies on the Xception architecture, which has been pre-trained on the ImageNet dataset and has been fully trained on whole-image BC according to mammograms. The convolutional design method is used in this paper, since it performs better than the other methods. On the prepared dataset, CoroNet was trained and tested. Experiments show that in a four-class classification, it may attain an overall accuracy of 94.92% (benign mass vs. malignant mass) and (benign calcification vs. malignant calcification). CoroNet has a classification accuracy of 88.67% for the two-class cases (calcifications and masses). The paper concluded that there are promising outcomes that could be improved because more training data are available. breast cancer; mammogram; coronet; deep learning; convolutional neural network; transfer learning +
Footnote †: journal: Medical Image Analysis  
as well as 10 million deaths (9.9 million excluding NMSC, excluding basal cell carcinoma) occurred in various countries of the world (Table 1). Figure 1 depicts the global distribution of new cases and fatalities for the 10 most common types of cancer among females worldwide in 2020 [3]. To detect the presence of cancer in the body, effective techniques are available. In the early stages, Breast Cancer (BC) can be detected through screening; hence, the treatment can be more effective. Several methods are available, including ultrasound, magnetic resonance imaging (MRI), CT, tomosynthesis, and mammography, as well as molecular breast imaging. Because mammography is cheap and available, it is considered the most widely adopted screening method. When examining a human breast, mammography employs low-dose X-rays. Notably, mammography is a simple and affordable method by specialists. Actually, it is considered the gold standard method of detecting the early stages of BC before the lesions turn into something clinically tangible. Its images show cancerous masses and calcium deposits more brightly. As a result, the death rate decreased by 25 to 30%. A specialist receives two views of the breast, producing two images, namely MLO (Medio Lateral Oblique) and CC (Cranio Caudal) views [4; 5]. Specialists have accomplished results of cancer detection that have varied broadly. Even the performances of top clinicians pave the way for further improvements [6; 7]. Al \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Location of Cancer**} & \multicolumn{2}{c}{**Number of New Cases (\% of**} & \multicolumn{2}{c}{**Number of New Deaths (\% of**} \\  & **All Locations)** & **All Locations)** \\ \hline Brain, nervous system & (1.6) & 308,102 & (2.5) & 251,329 \\ Colon & (6.0) & 1,148,515 & (5.8) & 576,858 \\
**Female breast** & **(11.7)** & **2,261,419** & **(6.9)** & **684,996** \\ Leukemia & (2.5) & 474,519 & (3.1) & 311,594 \\ Liver & (4.7) & 905,677 & (8.3) & 830,180 \\ Lung & (11.4) & 2,206,771 & (18.0) & 1,796,144 \\ Nommelanoma of skin & (6.2) & 1,198,073 & (0.6) & 63,731 \\ Ovary & (1.6) & 313,959 & (2.1) & 207,252 \\ Prostate & (7.3) & 1,414,259 & (3.8) & 375,304 \\ Stomach & (5.6) & 1,089,103 & (7.7) & 768,793 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: New cases and deaths for 10 cancer types in 2020.