Rifsya Aulia, Dina Pani Safira, Khaury Audilla, Raudhatul Khairiyah

Classification of Breast Cancer Ultrasound Images Using Convolutional Neural Network

Introduction

Classification of breast cancer ultrasound images using convolutional neural network. Research classifies breast cancer ultrasound images using CNNs (ResNet50V2, MobileNetV2). Achieved 92% accuracy with ResNet50V2+SGDM for critical early detection.

Abstract

Breast cancer ranks among the primary contributors to female mortality, thereby underscoring the critical importance of early detection. This research employs a deep learning approach based on Convolutional Neural Networks (CNNs) to classify breast cancer using ultrasound imagery, comparing the ResNet50V2 and MobileNetV2 architectures with three optimizers: Adam, RMSprop, and SGDM. The dataset used in this study is the Breast Ultrasound Images (BUSI) dataset, obtained from Kaggle, which comprises three diagnostic categories: benign, malignant, and normal. The research workflow encompassed several stages, including data acquisition, image pre-processing involving normalization and augmentation, and dataset partitioning using the Holdout Split method, with proportions of 70% for training, 15% for validation, and 15% for testing. The experimental findings revealed that the ResNet50V2 architecture combined with the SGDM optimizer achieved the best performance, recording accuracy, precision, recall, and F1-score values of 92%. Meanwhile, MobileNetV2 with RMSprop achieved the highest performance on its architecture with 86% accuracy, 88% precision, 86% recall, and 86% F1-score. These findings prove that CNN architecture selection and optimization algorithms have a significant influence on medical image classification performance.

Full Text

Comments

You need to be logged in to post a comment.