Efficient Breast Cancer Classification Using Histopathological Images and a Simpler VGG - Rodrigues Filho, Marcelo Luis; Cortes, Omar Andres Carmona (Anais do Brazilian e-Science Workshop (BreSci), 2021)
Breast cancer is the second most deadly disease worldwide. This severe condition led 627,000 people to die in 2018. Thus, early detection is critical for improving the patients' lifetime or even cure them. In this context, we can appeal to Medicine 4.0 that exploits the machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.