Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer

2013 ◽  
pp. 81-112 ◽  
Author(s):  
Kenji Suzuki
Keyword(s):  
Machine Learning ◽  
Colon Cancer ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 694
Author(s):  
Xuejiao Pang ◽  
Zijian Zhao ◽  
Ying Weng
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Practice ◽  
Gastrointestinal Endoscopy ◽  
Computer Aided Diagnosis ◽  
Learning Methods ◽  
Learning Abilities ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

scholarly journals Survey on machine learning algorithms for liver disease diagnosis and prediction

2018 ◽  
Vol 7 (1.8) ◽  
pp. 99 ◽  
Author(s):  
M Kiran Kumar ◽  
M Sreedevi ◽  
Y C. A. Padmanabha Reddy
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

Machine learning plays a vital role in health care industry. It is very important in Computer Aided Diagnosis. Computer Aided Diagnosis is a quickly developing dynamic region of research in medicinal industry. The current specialists in machine learning guarantee the enhanced precision of discernment and analysis of diseases. The computers are empowered to think by creating knowledge by learning. This procedure enables the computers to self-learn individually without being explicitly programed by the programmer .There are numerous sorts of Machine Learning Techniques and which are utilized to classify the data sets. They are Supervised, Unsupervised and Semi-Supervised, Reinforcement, deep learning algorithms. The principle point of this paper is to give comparative analysis of supervised learning algorithms in medicinal area and few of the techniques utilized as a part of liver disease prediction.

Computer Aided Diagnosis of Thyroid Disease Using Machine Learning Algorithms

2020 ◽  
Author(s):  
Md. Asfi-Ar-Raihan Asif ◽  
Mirza Muntasir Nishat ◽  
Fahim Faisal ◽  
Md. Fahim Shikder ◽  
Mahmudul Hasan Udoy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thyroid Disease ◽  
Learning Algorithms ◽  
Computer Aided Diagnosis ◽  
Machine Learning Algorithms ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine

2019 ◽  
Vol 52 (6) ◽  
pp. 387-396 ◽  
Author(s):  
Marcel Koenigkam Santos ◽  
José Raniery Ferreira Júnior ◽  
Danilo Tadao Wada ◽  
Ariane Priscilla Magalhães Tenório ◽  
Marcello Henrique Nogueira Barbosa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Diagnostic Imaging ◽  
Precision Medicine ◽  
Quantitative Information ◽  
Computer Aided Diagnosis ◽  
Rapid Expansion ◽  
Computer Aided ◽  
The Impact ◽  
Aided Diagnosis

Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.

Sa1655 UNSUPERVISED MACHINE LEARNING MODELS FOR POLYP IMAGE DATABASES USED FOR COMPUTER-AIDED DIAGNOSIS

2020 ◽  
Vol 158 (6) ◽  
pp. S-370-S-371
Author(s):  
Dennis Shung ◽  
John Onofrey ◽  
Artur Viana ◽  
Harry R. Aslanian
Keyword(s):  
Machine Learning ◽  
Computer Aided Diagnosis ◽  
Image Databases ◽  
Learning Models ◽  
Unsupervised Machine Learning ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Machine Learning Models

A Computer Aided Diagnosis System for Lung Cancer based on Statistical and Machine Learning Techniques

2014 ◽  
Vol 9 (2) ◽  
Author(s):  
Hamada R. H. Al-Absi ◽  
Brahim Belhaouari Samir ◽  
Suziah Sulaiman
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Computer Aided Diagnosis ◽  
Machine Learning Techniques ◽  
Diagnosis System ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

Machine Learning based Gastric Cancer Computer-aided Diagnosis System using Feature Selection

2020 ◽  
Vol 69 (1) ◽  
pp. 170-176
Author(s):  
Yun-ji Kim ◽  
Sin-ae Lee ◽  
Dong-hyun Kim ◽  
Jung-woo Chae ◽  
Hyun-sik Ham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Feature Selection ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis
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