Convolutional neural network based on transfer learning for breast cancer screening

TL;DR

This paper presents a deep convolutional neural network ensemble approach using transfer learning to improve breast cancer detection accuracy from ultrasound images, achieving 99.5% accuracy and 99.6% sensitivity.

Contribution

It introduces a novel parallel neural network fusion architecture with voting for breast cancer classification from ultrasound images, outperforming existing methods.

Findings

Achieved 99.5% accuracy on breast ultrasound dataset.

Attained 99.6% sensitivity in cancer detection.

Demonstrated superior performance over state-of-the-art algorithms.

Abstract

Breast cancer is the most common cancer in the world and the most prevalent cause of death among women worldwide. Nevertheless, it is also one of the most treatable malignancies if detected early. In this paper, a deep convolutional neural network-based algorithm is proposed to aid in accurately identifying breast cancer from ultrasonic images. In this algorithm, several neural networks are fused in a parallel architecture to perform the classification process and the voting criteria are applied in the final classification decision between the candidate object classes where the output of each neural network is representing a single vote. Several experiments were conducted on the breast ultrasound dataset consisting of 537 Benign, 360 malignant, and 133 normal images. These experiments show an optimistic result and a capability of the proposed model to outperform many state-of-the-art algorithms on several measures. Using k-fold cross-validation and a bagging classifier ensemble, we achieved an accuracy of 99.5% and a sensitivity of 99.6%.