Undoubtedly breast cancer identifies itself as one of the most widespread and
terrifying cancers across the globe. Millions of women are getting affected
each year from it. Breast cancer remains the major one for being the reason of
largest number of demise of women. In the recent time of research, Medical
Image Computing and Processing has been playing a significant role for
detecting and classifying breast cancers from ultrasound images and mammograms,
along with the celestial touch of deep neural networks. In this research, we
focused mostly on our rigorous implementations and iterative result analysis of
different cutting-edge modified versions of EfficientNet architectures namely
EfficientNet-V1 (b0-b7) and EfficientNet-V2 (b0-b3) with ultrasound image,
named as CEIMVEN. We utilized transfer learning approach here for using the
pre-trained models of EfficientNet versions. We activated the hyper-parameter
tuning procedures, added fully connected layers, discarded the unprecedented
outliers and recorded the accuracy results from our custom modified
EfficientNet architectures. Our deep learning model training approach was
related to both identifying the cancer affected areas with region of interest
(ROI) techniques and multiple classifications (benign, malignant and normal).
The approximate testing accuracies we got from the modified versions of
EfficientNet-V1 (b0- 99.15%, b1- 98.58%, b2- 98.43%, b3- 98.01%, b4- 98.86%,
b5- 97.72%, b6- 97.72%, b7- 98.72%) and EfficientNet-V2 (b0- 99.29%, b1-
99.01%, b2- 98.72%, b3- 99.43%) are showing very bright future and strong
potentials of deep learning approach for the successful detection and
classification of breast cancers from the ultrasound images at a very early
stage.

利用深度学习方法，通过超参数调整、完全连接层添加、异常值剔除等措施，用于检测和分析乳腺癌的超声图像，结果表明 EfficientNet 架构在乳腺癌早期检测和分型方面具有巨大潜力。

CEIMVEN：一种基于经修改的 EfficientNet（V1-V2）架构的尖端实现方法，用于乳腺癌超声图像的检测与分类

CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions  of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and  Classification from Ultrasound Images