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.

翻译：毫无疑问，乳腺癌是全球最普遍且最令人恐惧的癌症之一。每年有数百万女性受其影响。乳腺癌仍是导致女性死亡人数最多的主要癌症类型。在近期研究中，医学图像计算与处理技术结合深度神经网络的卓越能力，在基于超声图像和乳腺X线图像的乳腺癌检测与分类中发挥着重要作用。本研究重点聚焦于对EfficientNet系列架构不同前沿改进版本的严格实现与迭代结果分析，具体涵盖EfficientNet-V1（b0-b7）及EfficientNet-V2（b0-b3）架构与超声图像的结合，并将其命名为CEIMVEN。我们采用迁移学习方法，利用EfficientNet各版本的预训练模型。通过激活超参数调优流程、添加全连接层、剔除异常离群值，并记录自定义改进型EfficientNet架构的准确率结果。我们的深度学习模型训练方法涵盖基于感兴趣区域（ROI）技术的癌变区域识别以及多分类任务（良性、恶性、正常）。实验获得的近似测试准确率如下：EfficientNet-V1改进版（b0-99.15%，b1-98.58%，b2-98.43%，b3-98.01%，b4-98.86%，b5-97.72%，b6-97.72%，b7-98.72%）与EfficientNet-V2改进版（b0-99.29%，b1-99.01%，b2-98.72%，b3-99.43%）均展现出深度学习方法在基于超声图像的乳腺癌早期成功检测与分类中的光明前景与强大潜力。