Abstract
In this study, it is intended to increase the clas- sification accuracy results of malignant lymphoma images by evaluating spatial relations. As a first step, convolutional neural network (CNN) based features are extracted in the original RGB color space of digital histopathalogical images. Classification models of each feature vectors are obtained by employing CNN, support vector machines (SVM) and random forest (RF) methods. For comparison purposes, the classification accuracy results obtained from supervised learning methods are presented in the experimental results section.
Keywords
Convolutional neural network
Artificial intelligence
Computer science
Support vector machine
Pattern recognition (psychology)
RGB color model
Random forest
Feature vector
Feature (linguistics)
Artificial neural network
Kernel (algebra)
Mathematics
Subject Areas
AI in cancer detection
·Artificial Intelligence ·Physical Sciences
Radiomics and Machine Learning in Medical Imaging
·Radiology, Nuclear Medicine and Imaging ·Health Sciences
Digital Imaging for Blood Diseases
·Computer Vision and Pattern Recognition ·Physical Sciences
Citations by Year
2022: 1
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