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A study on RGB image multi-thresholding using Kapur/Tsallis entropy and moth-flame algorithm
(Universidad Internacional de la Rioja, 2021)
In the literature, a considerable number of image processing and evaluation procedures are proposed and
implemented in various domains due to their practical importance. Thresholding is one of the pre-processing
techniques, ...
A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier
(Computational Intelligence and Neuroscience, 2022)
A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and ...
G-Sep: A Deep Learning Algorithm for Detection of Long-Term Sepsis Using Bidirectional Gated Recurrent Unit
(International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2022)
Sepsis is a common and deadly condition that must be treated eloquently within 19 hours. Numerous deep learning techniques, including Recurrent Neural Networks, Convolution Neural Networks, Long Short-Term Memory, and Gated ...
A Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifier
(Association for Computing Machinery, 2021)
There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it ...
CNN supported framework for automatic extraction and evaluation of dermoscopy images
(Journal of Supercomputing, 2022)
Skin Cancer is one of the acute diseases listed under top 5 groups in 2020 report of World Health Organisation. This research aims to propose a Convolutional Neural Network framework to extract and evaluate the suspicious ...
An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection
(International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2023)
COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates ...
Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes
(Procedia Computer Science, 2022)
In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive ...
Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study
(Procedia Computer Science, 2022)
The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are ...
Deep and handcrafted feature supported diabetic retinopathy detection: A study
(Procedia Computer Science, 2022)
The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) ...
Automated segmentation of leukocyte from hematological images—a study using various CNN schemes
(Springer, 2022)
Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is ...