Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1002-1007. doi: 10.1109/EMBC48229.2022.9871866.
Breast Cancer has been the primary reason for mortality in women of age between twenties and sixties worldwide; moreover early detection and treatment provides patients to get absolute treatment and decrease the mortality rate. Furthermore, recent research indicates that most experienced physicians have plenty of limitations, hence the plethora of work has been carried out to develop an automated mechanism of segmentation and classification of affected area and type of cancer; however, it is still considered to be highly challenging due to the variability of tumor in shape, low signal to noise ratio, shape, size and location of tumor. Furthermore, mammographic mass segmentation and detection are performed as a separate task and a convolution neural network is a highly adopted architecture for the same. In this research, we have designed and developed unified CNN architecture to perform the segmentation and detection of a breast mass. The unified-CNN architecture comprises a novel module for convolution which is combined through additional offset. Further RRS aka Random Region Selection mechanism is applied for data augmentation approach and high-level feature map is implied to achieve the high prediction. Furthermore, unified-CNN is evaluated using the metrics like true positive Rate at FPI (False Positive per Image) and Dice Index on INBreast dataset, also comparative analysis is out carried with various existing methodology. Unified-CNN is developed through improvising CNN. It introduces a novel module at the convolution layer to aim for a high-level feature map in order to get a high prediction. RRS (Random Region Selection) algorithm is used as the data augmentation approach to select the boundary region of the affected area; further robust model training is designed and optimized for process to make optimal. Unified-CNN introduces novel module at the convolution layer to aim for high level feature map in order to get high prediction; further ROI pooling is utilized for boundary detection in images.
乳腺癌是全球 20 至 60 岁女性死亡的主要原因；此外，早期发现和治疗可为患者提供绝对治疗并降低死亡率。此外，最近的研究表明，大多数有经验的医生都有很多局限性，因此已经进行了大量的工作来开发一种自动分割和分类受影响区域和癌症类型的机制；然而，由于肿瘤形状的可变性、低信噪比、肿瘤的形状、大小和位置，这仍然被认为是极具挑战性的。此外，乳腺肿块的分割和检测是作为一个单独的任务来进行的，卷积神经网络是高度采用的架构。在这项研究中，我们设计并开发了一个统一的卷积神经网络架构来进行乳腺肿块的分割和检测。统一的卷积神经网络架构包括一个用于卷积的新模块，该模块通过附加的偏移量组合在一起。进一步的 RRS（随机区域选择）机制被应用于数据增强方法，高级特征图被用来实现高预测。此外，还使用了真正阳性率在 FPI（每幅图像的假阳性率）和 Dice 指数等指标来评估统一的卷积神经网络，还与各种现有的方法进行了比较分析。统一的卷积神经网络是通过改进卷积神经网络来开发的。它在卷积层引入了一个新的模块，目的是获得一个高级特征图，以获得一个高的预测。RRS（随机区域选择）算法被用作数据增强方法来选择受影响区域的边界区域；进一步设计并优化了稳健的模型训练过程以达到最优。统一的卷积神经网络在卷积层引入了一个新的模块，目的是获得一个高级特征图，以获得一个高的预测；进一步在图像中使用 ROI 池化进行边界检测。
