Transfer Learning Method Towards Lung and Colon Cancers Automated Analysis in Histopathological Images DOI
Marwen Sakli, Chaker Essid, M. Bassem Ben Salah

et al.

Published: Dec. 3, 2024

Language: Английский

A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer DOI

Seyed Masoud HaghighiKian,

Ahmad Shirinzadeh-Dastgiri,

Mohammad Vakili-Ojarood

et al.

Indian Journal of Surgical Oncology, Journal Year: 2024, Volume and Issue: 16(1), P. 257 - 278

Published: Sept. 5, 2024

Language: Английский

Citations

7
Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet DOI
Chukwuebuka Joseph Ejiyi, Zhen Qin, Victor Kwaku Agbesi

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 185, P. 109494 - 109494

Published: Dec. 4, 2024

Language: Английский

Citations

6
A Hybrid Deep Learning and Machine Learning Approach with Mobile-EfficientNet and Grey Wolf Optimizer for Lung and Colon Cancer Histopathology Classification DOI Open Access
Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, J A García-Rodríguez

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(22), P. 3791 - 3791

Published: Nov. 11, 2024

Lung and colon cancers are among the most prevalent lethal malignancies worldwide, underscoring urgent need for advanced diagnostic methodologies. This study aims to develop a hybrid deep learning machine framework classification of Colon Adenocarcinoma, Benign Tissue, Squamous Cell Carcinoma from histopathological images.

Language: Английский

Citations

5
Fluorescence microscopy and histopathology image based cancer classification using graph convolutional network with channel splitting DOI
Asish Bera, Debotosh Bhattacharjee, Ondřej Krejcar

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107400 - 107400

Published: Jan. 6, 2025

Language: Английский

Citations

0
Advanced colon cancer detection: Integrating context-aware multi-image fusion (Camif) in a multi-stage framework DOI Creative Commons

M.V.R. Vittal

Egyptian Informatics Journal, Journal Year: 2025, Volume and Issue: 29, P. 100609 - 100609

Published: Jan. 8, 2025

Language: Английский

Citations

0
Early Detection of Gynecological Malignancies Using Ensemble Deep Learning Models: ResNet50 and Inception V3 DOI Creative Commons

Chetna Vaid Kwatra,

Harpreet Kaur, Monika Mangla

et al.

Informatics in Medicine Unlocked, Journal Year: 2025, Volume and Issue: unknown, P. 101620 - 101620

Published: Jan. 1, 2025

Language: Английский

Citations

0
Immune profile and routine laboratory indicator-based machine learning for prediction of lung cancer DOI
Yi Huang, Kaijun Jiang, Xiaochen Wang

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 190, P. 110111 - 110111

Published: April 2, 2025

Language: Английский

Citations

0
Early Colon Cancer Prediction from Histopathological Images Using Enhanced Deep Learning with Confidence Scoring DOI

V.P. Gladis Pushparathi,

J Shajeena,

T. Kamalam

et al.

Cancer Investigation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: April 3, 2025

Colon Cancer (CC) arises from abnormal cell growth in the colon, which severely impacts a person's health and quality of life. Detecting CC through histopathological images for early diagnosis offers substantial benefits medical diagnostics. This study proposes NalexNet, hybrid deep-learning classifier, to enhance classification accuracy computational efficiency. The research methodology involves Vahadane stain normalization preprocessing Watershed segmentation accurate tissue separation. Teamwork Optimization Algorithm (TOA) is employed optimal feature selection reduce redundancy improve performance. Furthermore, NalexNet model structured with convolutional layers normal reduction cells, ensuring efficient representation high accuracy. Experimental results demonstrate that proposed achieves precision 99.9% an 99.5%, significantly outperforming existing models. contributes development automated computationally system, has potential real-world clinical implementation, aiding pathologists diagnosis.

Language: Английский

Citations

0
BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification DOI

Triyas Ghosh,

Soham Chakraborty,

Dmitrii Kaplun

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100 - 109

Published: Jan. 1, 2025

Language: Английский

Citations

0
Diagnostic and Prognostic Performance of Artificial Intelligence Models in Detecting Odontogenic Keratocysts from Histopathologic Images: A Systematic Review and Meta-Analysis DOI Open Access
Reyhaneh Shoorgashti, Farnaz Jafari,

Abbas Yazdanfar

et al.

Middle East Journal of Rehabilitation and Health Studies, Journal Year: 2025, Volume and Issue: 12(3)

Published: April 29, 2025

Context: Odontogenic keratocysts (OKCs) are aggressive jaw cysts characterized by a high recurrence rate, making accurate diagnosis critical for effective treatment. Recent advances in artificial intelligence (AI) have demonstrated potential enhancing diagnostic accuracy histopathology. However, the effectiveness of AI diagnosing OKCs has not yet been systematically reviewed. Objectives: This study aims to evaluate and prognostic performance models detecting histopathologic images. Methods: systematic review was conducted accordance with preferred reporting items reviews meta-analyses (PRISMA) guidelines. A comprehensive literature search performed across PubMed, Scopus, Embase, Google Scholar, ScienceDirect identify studies that utilized from Studies were eligible inclusion if they addressed PICO (patient/population, intervention, comparison, outcomes) framework, specifically investigating whether (I) can enhance (O) images (P). meta-analysis pool studies, Egger’s test assess publication bias. Results: total eight included review. The risk bias (ROB) generally low, few exceptions. pooled area under curve (AUC) 0.967 (95% CI: 0.957 - 0.978). sensitivity ranged 0.89 0.92, specificity 0.88 0.94. summary receiver operating characteristic (sROC) an AUC 0.93. yielded P-value 0.522, indicating no significant evidence also highlighted several limitations, including small sample sizes, lack external validation, limited interpretability models. Conclusions: Artificial models, particularly deep learning architectures, demonstrate

Language: Английский

Citations

0