###### 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 \(\times\) 50 \(\times\) 3 (Experimental Study 1) and another with 1116 images resized to 224 \(\times\) 224 \(\times\) 3 (Experimental Study 2). Both original (50 \(\times\) 50 \(\times\) 3) and rescaled (224 \(\times\) 224 \(\times\) 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. p 2020 2020 15 January 20  
process. Because pathological images are obtained in various complex and well-equipped hospitals, these procedures require a specialist physician with extensive knowledge and experience. The accuracy of the diagnosis depends largely on the pathologist's expertise in examining the sample taken from the breast and making a diagnosis. Artificial intelligence (AI)-supported software is needed to help diagnose breast cancer to prevent misdiagnosis and minimize the workload of specialist doctors. The AI-supported systems currently used to diagnose breast cancer have not yet fully resolved this issue. Consequently, reducing the incidence of cancer, an important problem in human health, is possible through the early diagnosis of cancer and application of appropriate treatment to the diagnosed patient. The early diagnosis of cancer and implementation of the necessary treatment are of great importance for treating cancer and reducing cancer-related mortality rates. To reduce these processes and increase the accuracy of disease diagnosis, many researchers have proposed various deep learning models for the diagnosis and classification of breast cancer [1]. In recent years, deep learning methods, particularly pre-trained models, have received significant interest in medical image analysis due to their ability to leverage these models and reduce the need for large, annotated datasets. These techniques speed up the development of reliable diagnostic tools by enabling the adaptation of well-known architectures, such VGG16, DenseNet, and ResNet, for domain-specific applications. Due to its lightweight design and lower computational complexity, MobileNet has become one of the most effective models among these architectures, making it especially well-suited for medical applications. MobileNet, which was designed using deep separable convolution layers, is well-suited for resource-constrained environments like real-time diagnostics and mobile health applications because it delivers excellent accuracy with fewer parameters. The growing use of MobileNet in healthcare demonstrates its ability to bridge the gap between cutting-edge diagnostic methods and real-world application, providing a viable solution for tasks like illness categorization and detection in radiographic and histopathological imaging with high accuracy [2]. The remaining layout of the presented paper for the breast cancer classification is structured as follows: Section 2 provides a detailed explanation of the state-of-the-art studies developed for breast cancer diagnosis.