Effectiveness Analysis of Deep Learning Methods for Breast Cancer Diagnosis Based on Histopathology Images

Korkmaz M., KAPLAN K.

APPLIED SCIENCES-BASEL, vol.15, no.3, 2025 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 3
  • Publication Date: 2025
  • Doi Number: 10.3390/app15031005
  • Journal Name: APPLIED SCIENCES-BASEL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Kocaeli University Affiliated: Yes

Abstract

The early detection of breast cancer is crucial for both accelerating the treatment process and preventing the spread of cancer. The accuracy of diagnosis is also significantly influenced by the experience of pathologists. Many studies have been conducted on the correct diagnosis of breast cancer to help specialists and increase the accuracy of diagnosis. This study focuses on classifying breast cancer using deep learning models, including pre-trained VGG16, MobileNet, DenseNet201, and a custom-built Convolutional Neural Network (CNN), with the final dense layer optimized via the particle swarm optimization (PSO) algorithm. The Breast Histopathology Images Dataset was used to evaluate the performance of the model, forming two datasets: one with 157,572 images at 50 x 50 x 3 (Experimental Study 1) and another with 1116 images resized to 224 x 224 x 3 (Experimental Study 2). Both original (50 x 50 x 3) and rescaled (224 x 224 x 3) images were tested. The highest success rate was obtained using the custom-built CNN model with an accuracy rate of 93.80% for experimental study 1. The MobileNet model yielded an accuracy of 95.54% for experimental study 2. The experimental results demonstrate that the proposed model exhibits promising, and superior classification accuracy compared to state-of-the-art methods across varying image sizes and dataset volumes.