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Novell flir11/30/2023 The most important challenges in breast cancer detection process are accurate segmentation of the breast area and classification of the breast tissue, which play an important role in image guiding surgery, radiological treatment, and clinical computer-assisted diagnosis. Breast cancer can affect both men and women, however women are diagnosed with the disease 100 times more frequently than men. Rectified Linear Activation Function IoU,īreast cancer is the second most frequent cancer in the world, following lung cancer, the fifth leading cause of cancer death and the major cause of cancer death among women. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: The author(s) received no specific funding for this work.Ĭompeting interests: The authors have declared that no competing interests exist. Received: AugAccepted: OctoPublished: October 21, 2022Ĭopyright: © 2022 A. PLoS ONE 17(10):Įditor: Robertas Damaševičius, Politechnika Slaska, POLAND Mohamed E, Gaber T, Karam O, Rashed EA (2022) A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.Ĭitation: A. The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. The U-Net results were as follows: global accuracy = 96.6%, mean accuracy = 96.5%, mean IoU = 92.07%, and mean BF score = 78.34%. The proposed pooling layer was evaluated using a benchmark thermogram database (DMR-IR) and its results compared with U-Net results which was used as base results. The VPB and the AVG-MAX VPB are plugged into the backbone CNNs networks, such as U-Net, AlexNet, ResNet18 and GoogleNet, to show the advantages in segmentation and classification tasks associated with breast cancer diagnosis from thermograms. It can collect informative features by using two types of pooling techniques, maximum and average pooling. Based on the novel VPB, we proposed a new pooling module called AVG-MAX VPB. The VPB makes the CNNs able to collect both global and local features by including long and narrow pooling kernels, which is different from the traditional pooling layer, that gathers features from a fixed square kernel. The proposed VPB consists of two data pathways, which focus on extracting features along horizontal and vertical orientations. In this paper, we propose a novel design for the pooling layer called vector pooling block (VPB) for the CCN algorithm. However, the effect of pooling layer was not studied efficiently in literature. Pooling is a main data processing step in CNN that decreases the feature maps’ dimensionality without losing major patterns. In recent years, convolutional neural networks (CNNs) have been successfully applied for the diagnosis of breast cancer using different imaging modalities. Breast cancer is the second most frequent cancer worldwide, following lung cancer and the fifth leading cause of cancer death and a major cause of cancer death among women.
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