Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging

Zerouaoui, Hasnae; Idri, Ali

doi:10.1007/s10916-020-01689-1

Cited by 60 publications

(45 citation statements)

References 92 publications

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“…In literature this procedure is often called “hand-crafting” and it is utilized to anticipate the right category, which is usually predicted features. It is broadly employed in many medical domains [ 91 ]. In this approach, features can be manually designed, or “by hand” aiming to handle particular problems such as variations and occlusions in scale and illumination.…”

Section: Discussionmentioning

confidence: 99%

“…GLCM, LBGLCM, GLRLM, and SFTA can be mined to categorize pandemic diseases [ 84 ]. GLCM is the most utilized approach to extract features regarding its good performance in many disciplines particularly in oncology imaging when the texture features of the images are easily detachable [ 91 , 92 ] DL approaches for FE are getting more attention regarding their high capability of extracting features [ 93 ]. Many researchers have adopted DL approaches and achieved outstanding performance in data extraction from images, regarding its capability to discover features on a new dataset on its own, contrasting to previous machine learning algorithms [ 61 , 94 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh¹,

Nilashi²,

Ismail³

et al. 2022

Journal of Infection and Public Health

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh¹,

Nilashi²,

Ismail³

et al. 2022

Journal of Infection and Public Health

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“…After the preprocessing, breast mass segmentation is the next crucial stage for increasing the accuracy of detection systems with decreasing false results about existence of abnormality [ 70 ]. Segmentation of breast tumor scans is challenging due to numerous obstacles like rough or lobulated corners, breast lumps or tissues, pectoral muscle, and mutual values of mass intensities.…”

Section: Breast Cancermentioning

confidence: 99%

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Ali

Pei

et al. 2021

Cancers

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Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.

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“…While AI was first proposed in 1956, the interest in AI probably increased significantly after AlphaGo (an AI-based computer program) defeated Lee Sedol, the world Go champion. AI has been applied in diverse settings, ranging from healthcare [1][2][3][4] to drug discovery [5] to (medical) image recognition [6][7][8][9] to automated driving [10,11] and so on. McKinsey Global Institute, for example, predicted that the AI market will grow to 13 trillion dollars by 2030 [12].…”

Section: Introductionmentioning

confidence: 99%

The Applications of Blockchain in Artificial Intelligence

Wang

Luo

Wen

et al. 2021

Security and Communication Networks

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There has been increased interest in applying artificial intelligence (AI) in various settings to inform decision-making and facilitate predictive analytics. In recent times, there have also been attempts to utilize blockchain (a peer-to-peer distributed system) to facilitate AI applications, for example, in secure data sharing (for model training), preserving data privacy, and supporting trusted AI decision and decentralized AI. Hence, in this paper, we perform a comprehensive review of how blockchain can benefit AI from these four aspects. Our analysis of 27 English-language articles published between 2018 and 2021 identifies a number of research challenges and opportunities.

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Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging

Cited by 60 publications

References 92 publications

Medical image processing and COVID-19: A literature review and bibliometric analysis

Medical image processing and COVID-19: A literature review and bibliometric analysis

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

The Applications of Blockchain in Artificial Intelligence

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