Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors

The liver is in charge of a plethora of tasks that are critical to healthy health. One of these roles is the conversion of food into protein and bile, which are both needed for digestion. Inhaled and possibly harmful chemicals are flushed from the body. It destroys numerous nutrients acquired through the gastrointestinal system and limits the release of cholesterol by utilizing vitamins, carbohydrates, and minerals stored in the liver. The body’s tissues are made up of tiny structures known as cells. Cells proliferate and divide in order to create new ones in the normal sequence of events. When an old or damaged cell has to be replaced, a new cell must be synthesized. In other circumstances, the procedure is a total and utter failure. If the tissues of dead or damaged cells that have been cleared from the body are not removed, they may give birth to nodules and tumors. The liver can produce two types of tumors: benign and malignant. Malignant tumors are more dangerous to one’s health than benign tumors. This article presents a technique for the classification and identification of liver cancers that is based on image processing and machine learning. The approach may be found here. During the preprocessing stage of picture creation, the fuzzy histogram equalization method is applied in order to bring about a reduction in image noise. After that, the photographs are divided into many parts in order to zero down on the area of interest. For this particular classification task, the RBF-SVM approach, the ANN method, and the random forest method are all applied.

show abstract

“…Each layer is composed of neuronal cells. A wide variety of ANN classifiers are utilized for the purpose of medical picture analysis [ 16 ].…”

Section: Literature Surveymentioning

confidence: 99%

Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor

Prasad

Prasad²,

Sawale³

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…DL is also termed as deep neural network or ANN with more hidden layers and complex structure. 11 Many studies have been employed in detection of cancer using different approaches and models of image processing and DL. Due to the availability of huge medical data, systems with high computational power and GPUs the developments of learning algorithms utilizing DL is gaining importance for disease diagnosis in the healthcare sector and particularly in oncology care.…”

Section: In Cancer Detection and Diagnosismentioning

confidence: 99%

“…Mao et al. presented the evolution, working principle and properties of artificial neural networks (ANNs), as well as the current state of research into the use of ANNs in the detection and diagnosis of gastrointestinal and liver malignancies 11 …”

Section: Introductionmentioning

confidence: 99%

A systematic review on deep learning‐based automated cancer diagnosis models

Tandon,

Agrawal,

Rathore

et al. 2024

J Cellular Molecular Medi

View full text Add to dashboard Cite

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL‐based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.

show abstract

“…A precise diagnosis serves as the foundation for an effective treatment plan in several subspecialties of dentistry, including maxillofacial surgery, orthodontics, and prosthodontics, amongst others [25]. Because it can identify Big data approach links between operational records as well as patterns in large data, machine learning makes it easier to diagnose and anticipate illnesses as well as assess the efficacy of different treatments [26][27][28].…”

Section: Use Of Ai In Dentistry Applications and Industrymentioning

confidence: 99%

Artificial intelligence in use of ZrO2 material in biomedical science

Singh

Verma

2022

J. Electrochem. Sci. Eng.

View full text Add to dashboard Cite

The rapidly growing discipline of artificial intelligence (AI) seeks to develop software and computers that can do tasks that have historically required the intelligence of people. Machine learning (ML) is a subfield of AI that makes use of algorithms to "learn" from data's innate statistical patterns and structures to extrapolate information that is otherwise hidden. A growing emphasis on cosmetic dentistry has coincided with ZrO2‘s rise to prominence as a result of its improved biocompatibility, visually pleasant look, strong oxidation resistance, better mechanical properties, and lack of documented allergic responses. Advances in the field of AI and ML have led to novel applications of ZrO2 in dental devices for biological objectives. Artificial intelligence (AI) technologies have attracted a lot of attention in ZrO2-related research and therapeutic applications due to their ability to analyze data and discover connections between seemingly unrelated events. Specifically, their incorporation into zirconia is largely responsible for this. Zirconia's versatility in the scientific community means that how AI is used in the area varies with the specific directions in which zirconia is utilized. Therefore, this article primarily focuses on the use of AI in the biomedical use of ZrO2 in dentistry.

show abstract

Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors

Cited by 22 publications

References 31 publications

Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor

Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor

A systematic review on deep learning‐based automated cancer diagnosis models

Artificial intelligence in use of ZrO2 material in biomedical science

Contact Info

Product

Resources

About