EDRNet: An attention-based model for multi-type tumor and polyp segmentation in medical imaging

Displays, Journal Year: 2025, Volume and Issue: unknown, P. 103031 - 103031

Published: March 1, 2025

Language: Английский

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ANALYSIS OF DIFFERENT POOLING FUNCTIONS ON A CONVOLUTION NEURAL NETWORK BASED MODEL

International Journal of 3D Printing Technologies and Digital Industry, Journal Year: 2024, Volume and Issue: 8(2), P. 266 - 276

Published: Aug. 29, 2024

The common denominator of deep learning models used in many different fields today is the pooling functions their internal architecture. These not only directly affect performance study, but also training time. For this reason, it extremely important to measure and share success values. In performances commonly soft pooling, max spatial pyramid average were measured on a dataset as benchmarking literature. purpose, new CNN based architecture was developed. Accuracy, F1 score, precision, recall categorical cross entropy metrics studies literature developed As result obtained, 97.79, 92.50, 91.60 89.09 values from best worst for accuracy obtained functions, respectively. light these results, study have provided better conceptual comparative understanding impact CNN-based model.

Language: Английский

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State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(12), P. 311 - 311

Published: Dec. 6, 2024

Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures tissues. Despite its importance, the process encounters significant challenges, including variability conditions, complex structures, artefacts (e.g., noise), which can compromise accuracy traditional methods. The emergence deep learning (DL) has catalyzed substantial advancements addressing these issues. This systematic literature review (SLR) provides comprehensive overview state-of-the-art DL methods developed over past six years microscopic images. We critically analyze key contributions, emphasizing how specifically tackle challenges cell, nucleus, tissue segmentation. Additionally, we evaluate datasets performance metrics employed studies. By synthesizing current identifying gaps existing approaches, this not only highlights transformative potential enhancing diagnostic research efficiency but also suggests directions future research. findings study have implications improving methodologies applications, ultimately fostering better patient outcomes advancing scientific understanding.

Language: Английский

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Improving Cell Image Segmentation by Using Isotropic Undecimated Wavelet Transform

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 159902 - 159912

Published: Jan. 1, 2024

Language: Английский

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White blood cell segmentation using U-Net and its variants to improve leukemia diagnosis

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 37(5), P. 3265 - 3286

Published: Dec. 12, 2024

Language: Английский

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Semantic Segmentation of Remote Sensing Images Based on U-Net

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 363 - 374

Published: Dec. 21, 2024

Language: Английский

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Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Language: Английский

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