Breast cancer has become a symbol of tremendous concern in the modern world, as it is one of the major causes of cancer mortality worldwide. In this concern, many people are frequently screening for breast cancer in order to be identified early and avert mortality from the disease by receiving treatment. Breast Ultrasonography Images are frequently utilized by doctors to diagnose breast cancer at an early stage. However, the complex artifacts and heavily noised Breast Ultrasonography Images make detecting Breast Cancer a tough challenge. Furthermore, the ever-increasing number of patients being screened for Breast Cancer necessitates the use of automated Computer Aided Technology for high accuracy diagnosis at a cheap cost and in a short period of time. The current progress of Artificial Intelligence (AI) in the fields of Medical Image Analysis and Health Care is a boon to humanity. In this study, we have proposed a compact integrated automated pipelining framework which integrates ultrasonography image preprocessing with Simple Linear Iterative Clustering (SLIC) to tackle the complex artifact of Breast Ultrasonography Images complementing semantic segmentation with Modified U-Net leading to Breast Tumor classification with robust feature extraction using a transfer learning approach with pretrained VGG 16 model and densely connected neural network architecture. The proposed automated pipeline can be effectively implemented to assist medical practitioners in making more accurate and timely diagnoses of breast cancer.
翻译:乳腺癌已成为现代世界极为关切的一个象征,因为它是全世界癌症死亡的主要原因之一。在这个关注中,许多人经常接受乳腺癌筛查,以便及早发现乳腺癌,并通过接受治疗避免疾病死亡。乳房超声成像经常被医生在早期阶段利用,以诊断乳腺癌。然而,复杂的人工制品和高度无声的乳房超声成像图像使检测乳腺癌成为一项艰巨的挑战。此外,越来越多的接受乳腺癌筛查的病人需要使用自动化计算机辅助技术进行高精度诊断,费用低廉,而且时间短。医学图像分析和保健领域的人工智能(AI)目前的进展是人类的荣耀。在本研究中,我们提议了一个集集超声图像前处理与简单线性热性循环聚合(SLIC)相结合的紧凑综合自动管内衬框架,以解决乳房超音层成像的复杂成像,与Modific UNet相补充,导致乳腺癌模型分类,在短期内进行稳健的心脏型诊断。在16个深度提取式医学结构中,可以有效地利用一个现代化的深度提取方法进行学习。