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GAPCNN with HyPar: Global Average Pooling convolutional neural network with novel NNLU activation function and HYBRID parallelism
With the increasing demand for deep learning in the last few years, CNNs have been widely
used in many applications and have gained interest in classification, regression, and image …
used in many applications and have gained interest in classification, regression, and image …
Hybrid metaheuristics with deep learning-based fusion model for biomedical image analysis
Biomedical image analysis has played a pivotal role in modern healthcare by facilitating
automated analysis and interpretation of medical images. Biomedical image classification is …
automated analysis and interpretation of medical images. Biomedical image classification is …
Revolutionizing diabetic retinopathy diagnosis through advanced deep learning techniques: Harnessing the power of GAN model with transfer learning and the …
Diabetic Retinopathy (DR) presents a substantial risk to vision, underscoring the critical
necessity for prompt identification and timely intervention to avert visual decline …
necessity for prompt identification and timely intervention to avert visual decline …
Compressed lightweight deep learning models for resource‐constrained Internet of things devices in the healthcare sector
The performance of convolutional neural networks (CNNs) in image classification and object
detection has been remarkable, even though they contain millions and billions of …
detection has been remarkable, even though they contain millions and billions of …
Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification
Batch Normalization is an important approach to advancing deep learning since it allows
multiple networks to train simultaneously. A problem arises when normalizing along the …
multiple networks to train simultaneously. A problem arises when normalizing along the …
Intelligent biomedical image classification in a big data architecture using metaheuristic optimization and gradient approximation
Medical imaging has experienced significant development in contemporary medicine and
can now record a variety of biomedical pictures from patients to test and analyze the illness …
can now record a variety of biomedical pictures from patients to test and analyze the illness …
A Transfer Learning-Based Model for Brain Tumor Detection in MRI Images
Brain tumors are life-threatening medical conditions characterized by abnormal cell
proliferation in or near the brain. Early detection is crucial for successful treatment. However …
proliferation in or near the brain. Early detection is crucial for successful treatment. However …
Convolutional Neural Networks (CNN) and DBSCAN Clustering for SARs-CoV Challenges: Complete Deep Learning Solution
Early diagnosis of Covid-19 is a challenging task requiring congruous clinical medical
imaging, which is a time-consuming process and suffers from accuracy problems due to …
imaging, which is a time-consuming process and suffers from accuracy problems due to …
Feature Extraction using Hybridized Transfer Learning Approach for Oral Cancer Diagnosis
N Bharanidharan, KK Abhinav… - … on Smart Electronics …, 2024 - ieeexplore.ieee.org
Oral cancer diagnosis at earlier stage is very crucial to decide the treatment procedure and
to avoid mortality due to this malignant disease. Histopathological imaging is one among …
to avoid mortality due to this malignant disease. Histopathological imaging is one among …
Analysis and Modeling of Software Reliability Using Deep Learning Methods
G Habib, S Qureshi - System Reliability and Security, 2023 - taylorfrancis.com
An essential attribute of evaluating a software product's quality is its reliability, which is an
integral part of software quality. It is challenging for the software industry to develop highly …
integral part of software quality. It is challenging for the software industry to develop highly …