Classification of Breast Cancer Ultrasound Images with Deep Learning-Based Models

Breast cancer is the type of cancer that affects women the most frequently in the world. Additionally, it is the highest cause of death for women. For the detection and treatment of breast cancer, there are numerous imaging techniques. For medical image analysts, making a diagnosis is arduous, routi...

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Bibliographic Details
Published in:	Engineering proceedings Vol. 31; no. 1; p. 8
Main Authors:	Fatih Uysal, Mehmet Murat Köse
Format:	Journal Article
Language:	English
Published:	MDPI AG 01-12-2022
Subjects:	breast cancer classification deep learning ultrasound images
Online Access:	Get full text
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Summary:	Breast cancer is the type of cancer that affects women the most frequently in the world. Additionally, it is the highest cause of death for women. For the detection and treatment of breast cancer, there are numerous imaging techniques. For medical image analysts, making a diagnosis is arduous, routine, time-consuming and tedious. Additionally, the growing volume of ultrasounds to interpret has overloaded practitioners and analysts. In the past, investigations have been performed using mammogram images. This research aims to take a different approach. The hypothesis is that by using artificial intelligence (AI) for ultrasound analysis, the process of computer-aided diagnosis (CAD) can be made more effective, interesting and free from subjectivity. The research’s purpose is to classify benign (non-cancerous), malignant (cancerous) and normal samples. The dataset contains 780 images in total. Data were split into 70% for training and 30% for validation. In this dataset, data augmentation and data preprocessing are also applied. Three models are used to classify samples. While ResNet50 scores 85.4% accuracy, ResNeXt50 scores 85.83%, and VGG16 scores 81.11%. Making the diagnosis by artificial intelligence will provide relief in the field of medicine. Computer vision models may be used in medicine. Therefore, providing more data and testing data more broadly will help improve the model.
ISSN:	2673-4591
DOI:	10.3390/ASEC2022-13791