Titlebook: Artificial Neural Networks in Pattern Recognition; 11th IAPR TC3 Worksh Ching Yee Suen,Adam Krzyzak,Nicola Nobile Conference proceedings 20

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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

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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

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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

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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

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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

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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

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Artificial Neural Networks in Pattern Recognition978-3-031-71602-7Series ISSN 0302-9743 Series E-ISSN 1611-3349