Adoption of combined detection technology of tumor markers via deep learning algorithm in diagnosis and prognosis of gallbladder carcinoma

Chang, Ying; Wu, Qian; Chi, Limin; Huo, Huaying; Li, Qiang

doi:10.1007/s11227-021-03843-z

Cited by 2 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also created a CNN for the acquired imaging records' functioning features. Chang et al [43] considered the utility of a backpropagation neural network and genetic algorithm in the detection and prediction of tumors' signs in gallbladder cancer patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method

Obaid,

Turki,

Bellaaj

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method

Obaid,

Turki,

Bellaaj

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study

Obaid,

Turki,

Bellaaj

et al. 2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

Gallbladder (GB) disease is a common pathology that needs correct and early diagnosis for the optimum medical treatment. Early diagnosis is crucial as any delay or misdiagnosis can worsen the patient situation. Incorrect diagnosis could also lead to an escalation in patient symptoms and poorer clinical outcomes. The use of Artificial Intelligence (AI) techniques, ranging from Machine Learning (ML) to Deep Learning (DL) to predict disease progression, identify abnormalities, and estimate mortality rates associated with GB disorders has increased over the past decade. To this end, this paper provides a comprehensive overview of the AI approaches used in the diagnosis of GB illnesses. This review compiles and compares relevant papers from the last decade to show how AI might enhance diagnostic precision, speed, and efficiency. Therefore, this survey gives researchers the opportunity to find out both the diagnosis of GB diseases and AI techniques in one place. The maximum accuracy rate by ML was when using SVM with 96.67%, whilst the maximum accuracy rate by DL was by utilising a unique structure of VGG, GoogleNet, ResNet, AlexNet and Inception with 98.77%. This could provide a clear path for further investigations and algorithm’s development to boost diagnostic results to improve the patient’s condition and choose the appropriate treatment.

show abstract

Adoption of combined detection technology of tumor markers via deep learning algorithm in diagnosis and prognosis of gallbladder carcinoma

Cited by 2 publications

References 29 publications

Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method

Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method

Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study

Contact Info

Product

Resources

About