Ghoushchi, Saeid JafarzadehRanjbarzadeh, RaminNajafabadi, Saeed AghasoleimaniOsgooei, ElnazTirkolaee, Erfan Babaee2021-12-072021-12-072021Jafarzadeh Ghoushchi, S., Ranjbarzadeh, R., Najafabadi, S. A., Osgooei, E., & Tirkolaee, E. B. (2021). An extended approach to the diagnosis of tumour location in breast cancer using deep learning. Journal of Ambient Intelligence and Humanized Computing, 1-11.1868-51371868-5145https://doi.org/10.1007/s12652-021-03613-yhttps://hdl.handle.net/20.500.12713/2307Breast cancer is one of the most common and deadly cancers in women. However, early detection increases the likelihood of survival by 100%. Radiologists use mammograms to take X-ray images of the breast to look for signs of tumour formation, such as breast masses. The purpose of detecting these signs using convolutional networks is a modern machine learning (ML) model that performs image segmentation in one learning step. Therefore, this study develops a new machine learning approach based on modified deep learning (DL) to diagnose the tumour location in breast cancer. In this study, the data obtained from the databases (BCDRD01) are developed and resized and divided into data sets. A simple architecture is used for the first group of experiments, one of which utilizes a weighted function to counter the class imbalance. At first, after visualizing the images and using the Gabor filter, the exact location of the breast tissue is determined. In the following, two other complicated network-based architectures (VGG) (9 layers and 2.9 million parameters) and remaining networks (10 layers and 0.9 million parameters) are employed for the following experiments. The results indicate that convolutional neural networks (CNNs) are an appropriate option for the separation of breast cancer lesions.eninfo:eu-repo/semantics/closedAccessConvolutional Neural NetworkDeep LearningDiagnosisBreast CancerArticleWOS:0007239914000032-s2.0-85120350518Q110.1007/s12652-021-03613-yQ1