Advanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model DOI Creative Commons

Aditya Pal,

Hari Mohan, Mohamed Ben Haj Frej

et al.

Life, Journal Year: 2024, Volume and Issue: 14(11), P. 1488 - 1488

Published: Nov. 15, 2024

The purpose of this research is to contribute the development approaches for classification and segmentation various gastrointestinal (GI) cancer diseases, such as dyed lifted polyps, resection margins, esophagitis, normal cecum, pylorus, Z line, ulcerative colitis. This relevant essential because current challenges related absence efficient diagnostic tools early diagnostics GI cancers, which are fundamental improving diagnosis these common diseases. To address above challenges, we propose a new hybrid model, U-MaskNet, combination U-Net Mask R-CNN models. Here, utilized pixel-wise instance segmentation, together forming solution classifying segmenting cancer. Kvasir dataset, includes 8000 endoscopic images validate proposed methodology. experimental results clearly demonstrated that novel model provided superior compared other well-known models, DeepLabv3+, FCN, DeepMask, well improved performance state-of-the-art (SOTA) including LeNet-5, AlexNet, VGG-16, ResNet-50, Inception Network. quantitative analysis revealed our outperformed achieving precision 98.85%, recall 98.49%, F1 score 98.68%. Additionally, achieved Dice coefficient 94.35% IoU 89.31%. Consequently, developed increased accuracy reliability in detecting cancer, it was proven can potentially be used process and, consequently, patient care clinical environment. work highlights benefits integrating opening way further medical image segmentation.

Language: Английский

Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques DOI
Hari Mohan, Joon Yoo, Serhii Dashkevych

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Language: Английский

Citations

0
Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances DOI Creative Commons
Mehmet Eren Ahsen

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 420 - 420

Published: Jan. 27, 2025

Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling robust analysis complex, high-dimensional, heterogeneous biomedical data. This paper explores how diverse bioinformatics tasks, differential expression analysis, network inference, somatic mutation calling, can be reframed as binary classification thereby providing a unifying framework for their analysis. Traditional single-method approaches often fail to generalize across datasets due differences distributions, noise levels, underlying biological contexts. Ensemble particularly unsupervised ensemble approaches, emerges compelling solution by integrating predictions from multiple algorithms leverage strengths mitigate weaknesses. review focuses on principles recent advancements with particular emphasis methods. These demonstrate ability address critical challenges such lack labeled integration operating different scales. Overall, this highlights transformative potential learning advancing predictive accuracy, robustness, interpretability applications.

Language: Английский

Citations

0
Highlighting the Advanced Capabilities and the Computational Efficiency of DeepLabV3+ in Medical Image Segmentation: An Ablation Study DOI Creative Commons

Ioannis Prokopiou,

Panagiota Spyridonos

BioMedInformatics, Journal Year: 2025, Volume and Issue: 5(1), P. 10 - 10

Published: Feb. 14, 2025

Background: In clinical practice, identifying the location and extent of tumors lesions is crucial for disease diagnosis treatment. Artificial intelligence, particularly deep neural networks, offers precise automated segmentation, yet limited data high computational demands often hinder its application. Transfer learning helps mitigate these challenges by significantly reducing costs, although applying models can still be resource intensive. This study aims to present flexible computationally efficient architecture that leverages transfer delivers highly accurate results across various medical imaging problems. Methods: We evaluated three datasets with varying similarities ImageNet: ISIC 2018 (skin lesions), CBIS-DDSM (breast masses), Shenzhen Montgomery CXR Set (lung segmentation). An ablation on tested pre-trained backbones, architectures, loss functions. Results: The optimal configuration—DeepLabV3+ a ResNet50 backbone Log-Cosh Dice loss—was validated remaining datasets, achieving state-of-the-art results. Conclusion: Computationally simpler architectures deliver robust performance without extensive resources, establishing DeepLabV3+ as baseline future studies. domain, enhancing quality more critical improving segmentation accuracy than increasing model complexity.

Language: Английский

Citations

0
Advanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model DOI Creative Commons

Aditya Pal,

Hari Mohan, Mohamed Ben Haj Frej

et al.

Life, Journal Year: 2024, Volume and Issue: 14(11), P. 1488 - 1488

Published: Nov. 15, 2024

The purpose of this research is to contribute the development approaches for classification and segmentation various gastrointestinal (GI) cancer diseases, such as dyed lifted polyps, resection margins, esophagitis, normal cecum, pylorus, Z line, ulcerative colitis. This relevant essential because current challenges related absence efficient diagnostic tools early diagnostics GI cancers, which are fundamental improving diagnosis these common diseases. To address above challenges, we propose a new hybrid model, U-MaskNet, combination U-Net Mask R-CNN models. Here, utilized pixel-wise instance segmentation, together forming solution classifying segmenting cancer. Kvasir dataset, includes 8000 endoscopic images validate proposed methodology. experimental results clearly demonstrated that novel model provided superior compared other well-known models, DeepLabv3+, FCN, DeepMask, well improved performance state-of-the-art (SOTA) including LeNet-5, AlexNet, VGG-16, ResNet-50, Inception Network. quantitative analysis revealed our outperformed achieving precision 98.85%, recall 98.49%, F1 score 98.68%. Additionally, achieved Dice coefficient 94.35% IoU 89.31%. Consequently, developed increased accuracy reliability in detecting cancer, it was proven can potentially be used process and, consequently, patient care clinical environment. work highlights benefits integrating opening way further medical image segmentation.

Language: Английский

Citations

0