An Automatic Breast Tumor Detection and Classification including Automatic Tumor Volume Estimation Using Deep Learning Technique

Labcharoenwongs, Prinda; Vonganansup, Suteera; Chunhapran, Orawan; Noolek, Duangjai; Yampaka, Tongjai

doi:10.31557/apjcp.2023.24.3.1081

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…A computerized analysis of breast ultrasound images for automatic breast tumor detection, classification, and volume estimation was developed in [79]. The Radiology Department at Thammasat University and the Queen Sirikit Center of Breast Cancer in Thailand provided breast ultrasound images.…”

Section: Identification and Segmentation Of Roismentioning

confidence: 99%

A Review of Artificial Intelligence in Breast Imaging

Al-Karawi,

Al-Zaidi,

Helael

et al. 2024

Tomography

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With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

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Section: Identification and Segmentation Of Roismentioning

confidence: 99%

A Review of Artificial Intelligence in Breast Imaging

Al-Karawi,

Al-Zaidi,

Helael

et al. 2024

Tomography

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“…Second, compared to traditional manual detection and other deep learning methods, object detection technology may detect the positions of HB more quickly from a large amount of image data, and process significant amounts of pathological images in a shorter time, improving clinical workflow efficiency. Third, the shapes, sizes, colours, and orientations are different, but object detection methods, such as R‐CNN 100 and faster R‐CNN, 82 utilize two‐stage detection methodology, which can better handle objects of different scales and achieve more precise positioning of complex targets. Finally, when faced with multiple different HB in the same image or field of view, object detection algorithms may maximize comprehensive benefits by locking onto multiple suspicious targets while consuming minimal computing memory (Figure 6).…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Zheng,

Sha,

Deng

et al. 2023

Liver International

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Metabolic dysfunction‐associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time‐consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

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