Vacuole Segmentation and Quantification in Liver Images of Wistar Rat

Deshmukh, Sanket; Lokhande, Avinash; Wasnik, Ratul; Singhal, Nitin

doi:10.1109/embc44109.2020.9176500

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The final model provides 100% accuracy and also good score across different metrics. [5] This paper presents a deep-learning-based framework for the segmentation of vacuoles in liver images and also study the correlation of automated quantification with expert pathologist's manual evaluation. [16]Mohammad Badri Tamam uses Naïve Bayes and KNN algorithms to solve predictive problems based on the results of testing for patients with liver disease or not using the python application.…”

Section: Literature Surveymentioning

confidence: 99%

Liver Disease Diagnosis Using Machine Learning

Deshmukh¹,

Sawant²,

Khopade³

et al. 2022

IJEAST

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It is critical to diagnose liver illness early on in order to receive the best therapy possible. Because of the modest symptoms, medical professionals find it difficult to forecast the disease in its early stages. Symptoms frequently appear when it is too late. To address this problem, our research will use machine learning to improve liver disease diagnosis. The major goal of this study is to employ classification algorithms to distinguish between liver patients and healthy people. Chemical components (bilirubin, albumin, proteins, alkaline phosphatase) present in the human body, as well as tests such as SGOT and SGPT, determine whether a person is a patient, or whether they need to be diagnosed. Excessive alcohol consumption, inhalation of toxic gases, eating of contaminated food, pickles, and medicines have all contributed to an increase in patients with liver disease. The goal of this research is to analyse prediction algorithms in order to relieve doctors of their workload

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Section: Literature Surveymentioning

confidence: 99%

Liver Disease Diagnosis Using Machine Learning

Deshmukh¹,

Sawant²,

Khopade³

et al. 2022

IJEAST

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Prediction of Liver Diseases with Machine Learning Method

Teke

2022

Sürdürülebilir Mühendislik Uygulamaları Ve Teknolojik Gelişmeler Dergisi

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Bu çalışmada karaciğer test sonuçlarının makine öğrenmesi algoritmalarından lojistik regresyon sınıflandırılmasına dayalı hastalık tahmin modeli çalışması yapılmıştır. Karaciğer insan vücudunda adeta bir fabrika gibi çalışmaktadır. Bu organın hastalanması bütün vücuda zarar veren birçok etki meydana getirmektedir. Bu çalışmada belirli ölçütlere ve parametrelere göre bu hayati organ için hastalık tahmin modeli gerçekleştirilmiştir. Çalışmada karaciğere ait protein, albümin ve bilurubin gibi değerler hastalık tahmin modelinde incelenmiştir. Çalışmada kullanılan veri modeli açık kaynaklı kaggle web sitesinden alınmıştır. Tahmin modeli python dili ile jupyter notebook ortamında gerçekleştirilmiştir. Kategorik veri tahmini içinse lojistik regresyon modeli tercih edilmiştir. Oluşturulan model %84 doğruluk içermiştir. Değerlendirme ölçütü olarak karmaşıklık matrisi kullanılmış ve çalışmada sunulmuştur.

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Vacuole Segmentation and Quantification in Liver Images of Wistar Rat

Cited by 2 publications

References 13 publications

Liver Disease Diagnosis Using Machine Learning

Liver Disease Diagnosis Using Machine Learning

Prediction of Liver Diseases with Machine Learning Method

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