Breast Cancer Detection Using Deep Learning Technique Based On Ultrasound Image

TL;DR

This paper presents a deep learning system that enhances ultrasound image analysis for breast cancer detection, achieving high accuracy by combining image processing, segmentation, and feature extraction techniques.

Contribution

It introduces a novel deep learning approach that improves classification accuracy of breast cancer types from ultrasound images, surpassing previous methods.

Findings

Achieved 99.29% classification accuracy.

Enhanced image quality through processing techniques.

Effective segmentation and feature extraction methods.

Abstract

Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast. The major challenge that faces deep learning diagnosis of breast cancer was its shape, size and position non-uniformity especially malignant cancer. This work proposed a deep learning system that increased the accuracy of classification of breast cancer types from ultrasound images. It reaches 99.29% accuracy, exceeding other previous work. First, image processing was applied to in enhance the quality of input images. Second, the image segmentation was performed using U-Net architecture. Third, many features are extracted using Mobilenet. Finally, the accuracy of proposed system was evaluated.