Image segmentation with Cellular Automata DOI Creative Commons
Cesar Ascencio-Piña,

Sonia García-De-Lira,

Erik Cuevas

и другие.

Heliyon, Год журнала: 2024, Номер 10(10), С. e31152 - e31152

Опубликована: Май 1, 2024

Image segmentation is a computer vision technique that involves dividing an image into distinct and meaningful regions or segments. The objective was to partition the areas share similar visual characteristics. Noise undesirable artifacts introduce inconsistencies irregularities in data. These severely affect ability of most algorithms distinguish between true features, leading less reliable lower-quality results. Cellular Automata (CA) computational concept consists grid cells, each which can be finite number states. cells evolve over discrete time steps based on set predefined rules dictate how cell's state changes according its own states neighboring cells. In this paper, new approach CA model introduced. proposed consisted three phases. initial two phases process, primary eliminate noise interfere with identification exhibiting To achieve this, designed modify value cell pixel elements. third phase, element assigned chosen from directly represent final values for corresponding method evaluated using different images, considering important quality indices. experimental results indicated produces better-segmented images terms robustness.

Язык: Английский

Integrated ensemble CNN and explainable AI for COVID-19 diagnosis from CT scan and X-ray images DOI Creative Commons

Reenu Rajpoot,

Mahesh Gour, Sweta Jain

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 23, 2024

In light of the ongoing battle against COVID-19, while pandemic may eventually subside, sporadic cases still emerge, underscoring need for accurate detection from radiological images. However, limited explainability current deep learning models restricts clinician acceptance. To address this issue, our research integrates multiple CNN with explainable AI techniques, ensuring model interpretability before ensemble construction. Our approach enhances both accuracy and by evaluating advanced on largest publicly available X-ray dataset, COVIDx CXR-3, which includes 29,986 images, CT scan dataset SARS-CoV-2 Kaggle, a total 2,482 We also employed additional public datasets cross-dataset evaluation, thorough assessment performance across various imaging conditions. By leveraging methods including LIME, SHAP, Grad-CAM, Grad-CAM++, we provide transparent insights into decisions. model, DenseNet169, ResNet50, VGG16, demonstrates strong performance. For image sensitivity, specificity, accuracy, F1-score, AUC are recorded at 99.00%, 0.99, respectively. these metrics 96.18%, 0.9618, 0.96, methodology bridges gap between precision in clinical settings combining diversity explainability, promising enhanced disease diagnosis greater

Язык: Английский

Процитировано

5
An enhanced tree-seed algorithm for global optimization and neural architecture search optimization in medical image segmentation DOI
Zenglin Qiao, Lingyu Wu, Ali Asghar Heidari

и другие.

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107457 - 107457

Опубликована: Янв. 8, 2025

Язык: Английский

Процитировано

0
SG-UNet: Hybrid self-guided transformer and U-Net fusion for CT image segmentation DOI

Chunjie Lv,

Biyuan Li,

Gaowei Sun

и другие.

Journal of Visual Communication and Image Representation, Год журнала: 2025, Номер unknown, С. 104416 - 104416

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
MDSTransUNet: Multi-Scale Deep Supervised Transformer U-Net for COVID-19 Lung and Infection Segmentation DOI

Yidan Yan,

Beibei Hou, Junding Sun

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review DOI
Somayeh Sadat Mehrnia,

Zhino Safahi,

Amin Mousavi

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Март 4, 2025

The increasing rates of lung cancer emphasize the need for early detection through computed tomography (CT) scans, enhanced by deep learning (DL) to improve diagnosis, treatment, and patient survival. This review examines current prospective applications 2D- DL networks in CT segmentation, summarizing research, highlighting essential concepts gaps; Methods: Following Preferred Reporting Items Systematic Reviews Meta-Analysis guidelines, a systematic search peer-reviewed studies from 01/2020 12/2024 on data-driven population segmentation using structured data was conducted across databases like Google Scholar, PubMed, Science Direct, IEEE (Institute Electrical Electronics Engineers) ACM (Association Computing Machinery) library. 124 met inclusion criteria were analyzed. LIDC-LIDR dataset most frequently used; finding particularly relies supervised with labeled data. UNet model its variants used models medical image achieving Dice Similarity Coefficients (DSC) up 0.9999. reviewed primarily exhibit significant gaps addressing class imbalances (67%), underuse cross-validation (21%), poor stability evaluations (3%). Additionally, 88% failed address missing data, generalizability concerns only discussed 34% cases. emphasizes importance Convolutional Neural Networks, UNet, analysis advocates combined 2D/3D modeling approach. It also highlights larger, diverse datasets exploration semi-supervised unsupervised enhance automated diagnosis detection.

Язык: Английский

Процитировано

0
Image segmentation with Cellular Automata DOI Creative Commons
Cesar Ascencio-Piña,

Sonia García-De-Lira,

Erik Cuevas

и другие.

Heliyon, Год журнала: 2024, Номер 10(10), С. e31152 - e31152

Опубликована: Май 1, 2024

Image segmentation is a computer vision technique that involves dividing an image into distinct and meaningful regions or segments. The objective was to partition the areas share similar visual characteristics. Noise undesirable artifacts introduce inconsistencies irregularities in data. These severely affect ability of most algorithms distinguish between true features, leading less reliable lower-quality results. Cellular Automata (CA) computational concept consists grid cells, each which can be finite number states. cells evolve over discrete time steps based on set predefined rules dictate how cell's state changes according its own states neighboring cells. In this paper, new approach CA model introduced. proposed consisted three phases. initial two phases process, primary eliminate noise interfere with identification exhibiting To achieve this, designed modify value cell pixel elements. third phase, element assigned chosen from directly represent final values for corresponding method evaluated using different images, considering important quality indices. experimental results indicated produces better-segmented images terms robustness.

Язык: Английский

Процитировано

0