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State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)

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Abstract

Early detection of breast cancer is crucial for a better prognosis. Various studies have been conducted where tumor lesions are detected and localized on images. This is a narrative review where the studies reviewed are related to five different image modalities: histopathological, mammogram, magnetic resonance imaging (MRI), ultrasound, and computed tomography (CT) images, making it different from other review studies where fewer image modalities are reviewed. The goal is to have the necessary information, such as pre-processing techniques and CNN-based diagnosis techniques for the five modalities, readily available in one place for future studies. Each modality has pros and cons, such as mammograms might give a high false positive rate for radiographically dense breasts, while ultrasounds with low soft tissue contrast result in early-stage false detection, and MRI provides a three-dimensional volumetric image, but it is expensive and cannot be used as a routine test. Various studies were manually reviewed using particular inclusion and exclusion criteria; as a result, 91 recent studies that classify and detect tumor lesions on breast cancer images from 2017 to 2022 related to the five image modalities were included. For histopathological images, the maximum accuracy achieved was around 99 \(\%\), and the maximum sensitivity achieved was 97.29 \(\%\) by using DenseNet, ResNet34, and ResNet50 architecture. For mammogram images, the maximum accuracy achieved was 96.52 \(\%\) using a customized CNN architecture. For MRI, the maximum accuracy achieved was 98.33 \(\%\) using customized CNN architecture. For ultrasound, the maximum accuracy achieved was around 99 \(\%\) by using DarkNet-53, ResNet-50, G-CNN, and VGG. For CT, the maximum sensitivity achieved was 96 \(\%\) by using Xception architecture. Histopathological and ultrasound images achieved higher accuracy of around 99 \(\%\) by using ResNet34, ResNet50, DarkNet-53, G-CNN, and VGG compared to other modalities for either of the following reasons: use of pre-trained architectures with pre-processing techniques, use of modified architectures with pre-processing techniques, use of two-stage CNN, and higher number of studies available for Artificial Intelligence (AI)/machine learning (ML) researchers to reference. One of the gaps we found is that only a single image modality is used for CNN-based diagnosis; in the future, a multiple image modality approach can be used to design a CNN architecture with higher accuracy.

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Funding

This research was based upon work supported by the National Science Foundation (NSF) under Grant No. 2153430.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, MA, USA

    Pratibha Harrison

  2. Department of Mechanical Engineering, Khulna University of Engineering & Technology, PhulBari Gate, Khulna, 9203, Bangladesh

    Rakib Hasan

  3. Department of Mechanical Engineering, University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, MA, USA

    Kihan Park

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Contributions

Study conception and design were performed by Kihan Park and Pratibha Harrison. Material preparation, data collection, and analysis were performed by Pratibha Harrison and Rakib Hasan. The first draft of the manuscript was written by Pratibha Harrison and Rakib Hasan, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kihan Park.

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The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Harrison, P., Hasan, R. & Park, K. State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs). J Healthc Inform Res 7, 387–432 (2023). https://doi.org/10.1007/s41666-023-00144-3

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