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Undoubtedly breast cancer identifies itself as one of the most widespread andterrifying cancers across the globe. Millions of women are getting affectedeach year from it. Breast cancer remains the major one for being the reason oflargest number of demise of women. In the recent time of research, MedicalImage Computing and Processing has been playing a significant role fordetecting and classifying breast cancers from ultrasound images and mammograms,along with the celestial touch of deep neural networks. In this research, wefocused mostly on our rigorous implementations and iterative result analysis ofdifferent cutting-edge modified versions of EfficientNet architectures namelyEfficientNet-V1 (b0-b7) and EfficientNet-V2 (b0-b3) with ultrasound image,named as CEIMVEN. We utilized transfer learning approach here for using thepre-trained models of EfficientNet versions. We activated the hyper-parametertuning procedures, added fully connected layers, discarded the unprecedentedoutliers and recorded the accuracy results from our custom modifiedEfficientNet architectures. Our deep learning model training approach wasrelated 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 ofEfficientNet-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 strongpotentials of deep learning approach for the successful detection andclassification of breast cancers from the ultrasound images at a very earlystage.

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