Automatic Detection of Colorectal Polyps Using Transfer Learning

Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative study for a therapeutic vaccine comprises the investigation of gold nanoparticles and their influence on the immune response for the annihilation of cancer cells. The model is intended to be realized using Quantitative-Structure Activity Relationship (QSAR) methods, explicitly artificial neural networks combined with fuzzy rules, to enhance automated properties of neural nets with human perception characteristics. Image processing techniques such as morphological transformations and watershed segmentation are used to extract and calculate certain molecular characteristics from hyperspectral images. The quantification of single-cell properties is one of the key resolutions, representing the treatment efficiency in therapy of colon and rectum cancerous conditions. This was accomplished by using manually counted cells as a reference point for comparing segmentation results. The early findings acquired are conclusive for further study; thus, the extracted features will be used in the feature optimization process first, followed by neural network building of the required model.

show abstract

“…These results are strongly related to the authors' research in the area of cancer diagnosis and prevention [31,32].…”

supporting

confidence: 64%

Modelling in Synthesis and Optimization of Active Vaccinal Components

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The kvasir dataset is used to train the network. [45] A summary of existing methods, the corresponding reference and the best obtained performances is presented in Table II.…”

Section: B Ai Diagnosis For Colorectal Cancermentioning

confidence: 99%

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

et al. 2022

Self Cite

View full text Add to dashboard Cite

Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis. Neural Networks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities. In image processing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods. The analyzed factors are training parameters, image processing technique and the obtained performances. This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions. INDEX TERMSCancer diagnosis, Image processing, Neural networks I.

show abstract

“…One is that the upper network's learning rate is decreased because it must constantly adapt to changes in the distribution of input data. Second, network convergence is slowed down for the activation function enters the gradient saturation zone [21]. Batch normalization provides an efficient way to transform an output signal into an optimum range.…”

Section: Batch Normalizationmentioning

confidence: 99%

“…Dulf et al [70] used CNN design to quickly extract components. The extracted highlights were then sent to SVM for detection of provocative gastrointestinal disease in WCE recordings.…”

Section: Detection Of Colon Diseasementioning

confidence: 99%

An Outlook of Medical Image Analysis via Transfer Learning Approaches

Nagaraju¹,

Rao²

2022

View full text Add to dashboard Cite

Artificial intelligence advancements, particularly deep learning algorithms, are helpful for identifying, classifying, and rating designs in clinical images. Clinical diagnosis and scientific research both rely heavily on medical image analysis. The diagnosis of medical conditions frequently involves medical image acquisition techniques like pathology, computed tomography, magnetic resonance imaging, ultrasound, and x-ray. Transfer learning stands out from other deep learning techniques thanks to its simplicity, effectiveness, affordable training costs, and capacity to escape the dataset curse. The diagnosis of anomalies including Alzheimer's disease, diabetic retinopathy, colon cancer, breast cancer, and pulmonary nodule can be made using the medical imaging techniques combined with datasets and computer vision. These approaches are helpful in non-invasive qualitative and quantitative analysis on patients. However, labelling in medical images are still scarce. This paper mainly reviews the application of transfer learning in medical image analysis. Beginners can benefit from this review paper's guidance as they gain a thorough and organized understanding of transfer learning applications for the analysis of medical images. By establishing laws that will aid future advancements in medical image processing, policymakers in adjacent sectors will also profit from the trend of transfer learning in medical imaging.

show abstract

Automatic Detection of Colorectal Polyps Using Transfer Learning

Cited by 23 publications

References 43 publications

Modelling in Synthesis and Optimization of Active Vaccinal Components

Modelling in Synthesis and Optimization of Active Vaccinal Components

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

An Outlook of Medical Image Analysis via Transfer Learning Approaches

Contact Info

Product

Resources

About