CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images (2023-08-25T00:00:00.000000Z)

TL;DR

This research focused mostly on the rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet architectures namely EfficientNet-V1 and EfficientNet-V2 with ultrasound image, named as CEIMVEN, and utilized transfer learning approach here for using the pre-trained models of EfficientNet versions.

Abstract

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. The code for this research is available here: https://github.com/ac005sheekar/CEIMVEN-Breast.

References27 items

Immunotherapy in breast cancer: an overview of current strategies and perspectives

Emerging application of magnetic nanoparticles for breast cancer therapy

Targeting HER2-positive breast cancer: advances and future directions

Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network

Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques

CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis

Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning

Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network

A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images

MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion

Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data

Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis

An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)

Convolutional neural network-based models for diagnosis of breast cancer

Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers

Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network

Automated Segmentation of the Clinical Target Volume in the Planning CT for Breast Cancer Using Deep Neural Networks

Breast Cancer Malignancy Prediction Using Deep Learning Neural Networks

Visualization for Histopathology Images using Graph Convolutional Neural Networks

A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection

Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification

Images data practices for Semantic Segmentation of Breast Cancer using Deep Neural Network

Dataset of breast ultrasound images

Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. 18(1), pp. e0280148