declamation
发表于 2025-3-23 12:47:04
Timothy P Hughes,David M Ross,Junia V Melollenge. In this paper, we introduce a hybrid evolutionary algorithm that advances the design of CNNs for medical image segmentation. By integrating Cartesian Genetic Programming with Simulated Annealing, our approach efficiently explores the architectural design space, yielding CNN architectures tha
Ornithologist
发表于 2025-3-23 16:57:49
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Legion
发表于 2025-3-23 21:35:52
Handbook of Classical Conditioningre called lymphoblasts. It occurs when the bone marrow contains 20% or more lymphoblasts. Therefore, Leukemia is diagnosed by counting White Blood Cells (WBCs) in the microscopic smears of bone marrow and blood. There are several attempts for effective leukemia classification with the help of comput
NOVA
发表于 2025-3-23 23:25:55
Esther Hoffmann,Patrick Schöpflin. Melanoma, a malignant skin cancer, has the highest mortality rate among all skin cancer types. Early detection of melanoma significantly enhances the chances of effective treatment and survival rates. This research evaluates advanced deep learning techniques in medical imaging, specifically Vision
Cytokines
发表于 2025-3-24 05:57:23
Cynthia Arantes Ferreira Ludererge analysis-based computer-aided diagnostic (CAD) systems for oral cancer. To this end, we propose a novel model that we name Gray Wolf Optimization (GWO) based deep Feature Selection Network (GFS-Net). Initially, we use an attention-aided NASNet Mobile, a convolutional neural network (CNN) architec
无瑕疵
发表于 2025-3-24 09:18:14
Leticia Andrea Chechi,Cátia Grisaachine learning approach to predict the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III scores, quantifying motor symptom progression in PD patients. Using the longitudinal Parkinson’s Progression Markers Initiative (PPMI) dataset, we examined the impact of da
Narcissist
发表于 2025-3-24 12:31:08
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BALK
发表于 2025-3-24 17:20:10
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Torrid
发表于 2025-3-24 19:41:32
Konstantinos Demertzis,Lazaros Iliadishallenges. To bridge this gap, we introduce VAeViT, a pioneering hybrid model that seamlessly integrates the strengths of Vision Transformers and Variational Autoencoders (VAE). VAeViT leverages VAE’s efficiency in feature representation to encode the 3D object views into a lower-dimensional latent
parasite
发表于 2025-3-25 01:47:40
Artificial Neural Networks in Pattern Recognition978-3-031-71602-7Series ISSN 0302-9743 Series E-ISSN 1611-3349