Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images
Computed tomography (CT) scans, or radiographic images, were used to aid in the early
diagnosis of patients and detect normal and abnormal lung function in the human chest …
diagnosis of patients and detect normal and abnormal lung function in the human chest …
[HTML][HTML] Application of a novel deep learning technique using CT images for COVID-19 diagnosis on embedded systems
Problem A novel coronavirus (COVID-19) has created a worldwide pneumonia epidemic,
and it's important to make a computer-aided way for doctors to use computed tomography …
and it's important to make a computer-aided way for doctors to use computed tomography …
Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network
Abstract Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that
play a pivotal role in human health and are vital indicators of diseases such as malaria …
play a pivotal role in human health and are vital indicators of diseases such as malaria …
COVID-19 Image Classification: A Comparative Performance Analysis of Hand-Crafted vs. Deep Features
S Alinsaif - Computation, 2024 - mdpi.com
This study investigates techniques for medical image classification, specifically focusing on
COVID-19 scans obtained through computer tomography (CT). Firstly, handcrafted methods …
COVID-19 scans obtained through computer tomography (CT). Firstly, handcrafted methods …
Exploring deep echo state networks for image classification: A multi-reservoir approach
EJ López-Ortiz, M Perea-Trigo, LM Soria-Morillo… - Neural Computing and …, 2024 - Springer
Echo state networks (ESNs) belong to the class of recurrent neural networks and have
demonstrated robust performance in time series prediction tasks. In this study, we …
demonstrated robust performance in time series prediction tasks. In this study, we …
Image-based 3D reconstruction and permeability modelling of rock using enhanced interpretable deep residual learning
S Lin, M Dong, Z Liang, H Guo, H Zheng - Engineering Analysis with …, 2024 - Elsevier
The study presents a novel deep residual learning framework with interpretability for the
prediction of rock permeability. The proposed framework, termed ResNet-NiN integrates a …
prediction of rock permeability. The proposed framework, termed ResNet-NiN integrates a …
Object Detection Algorithms to Identify Skeletal Components in Carbonate Cores
HL Dawson, CM John - Marine and Petroleum Geology, 2024 - Elsevier
Identification of constituent grains in carbonate rocks requires specialist experience. A
carbonate sedimentologist must be able to distinguish between skeletal grains that change …
carbonate sedimentologist must be able to distinguish between skeletal grains that change …
Semantic segmentation for tooth cracks using improved DeepLabv3+ model
Objective Accurate and prompt detection of cracked teeth plays a critical role for human oral
health. The aim of this paper is to evaluate the performance of a tooth crack segmentation …
health. The aim of this paper is to evaluate the performance of a tooth crack segmentation …
A Deep Learning Approach for Arabic Manuscripts Classification
LS Al-Homed, KM Jambi, HM Al-Barhamtoshy - Sensors, 2023 - mdpi.com
For centuries, libraries worldwide have preserved ancient manuscripts due to their immense
historical and cultural value. However, over time, both natural and human-made factors have …
historical and cultural value. However, over time, both natural and human-made factors have …
Improved Skin Disease Classification with Mask R-CNN and Augmented Dataset
Skin diseases are a significant global health concern, impacting millions worldwide. Severe
diseases like psoriasis and dermatitis can coexist with more benign skin issues like acne …
diseases like psoriasis and dermatitis can coexist with more benign skin issues like acne …