Deep Learning Techniques for Breast Cancer Mitotic Cell Detection

Li, Jiquan; Qiu, Liqing; Yang, Yiming; Wang, Zhou

doi:10.1109/iccwamtip56608.2022.10016492

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…RF classifier processing takes 6.25 seconds [1] . J. Li et al proposes the invasion of tumours can be assessed using the pathological diagnostic of diabetes and cancer which also offers crucial information for precise diagnosis and treatment [2] . Cancer proliferation is one of the main signs of the disease, according to statistics, is the proportion of cells in mitosis.…”

Section: Introductionmentioning

confidence: 99%

An Overview on Revolutionizing Multi Disease Prediction System: Opportunity through Artificial Intelligence

Ulla

2024

IJRASET

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A revolutionary change towards proactive and individualised healthcare management has been sparked by the introduction of machine learning technology in the modern healthcare environment. In order to predict numerous diseases at once, this study presents the numerous Disease Prediction System (MDPS), a novel method that uses cutting-edge machine learning techniques to concurrently predict the likelihood of diabetes (both Type 1 and Type 2), Parkinson's disease, and heart diseases and create a thorough health profile for each person, the MDPS incorporates data from a wide variety of sources, such as genetic information, health records, lifestyle factors, and environmental information. diseases. To this end, the system makes use of advanced machine learning models, including ensemble approaches, deep learning, and Bayesian networks. The interface is easy to use, making it easier for both individuals and healthcare professionals to interpret the results. This promotes proactive disease prevention, efficient resource allocation, customised healthcare programmes, and research possibilities to better understand complicated health dynamics.

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Section: Introductionmentioning

confidence: 99%