Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e30625 - e30625

Published: May 1, 2024

Automatic classification of colon and lung cancer images is crucial for early detection accurate diagnostics. However, there room improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 D2) emphasizes their effectiveness in classifying from diverse images. It also highlights resilience, efficiency, superior performance across multiple datasets. These were tested on various types datasets, including NCT-CRC-HE-100K (set 100,000 non-overlapping image patches hematoxylin eosin (H&E) stained histological human colorectal (CRC) normal tissue), CRC-VAL-HE-7K 7180 N=50 patients with adenocarcinoma, no overlap NCT-CRC-HE-100K), LC25000 (Lung Colon Cancer Histopathological Image), IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center Diseases), showcasing cancers histopathological Computed Tomography (CT) scan underscores the multi-modal capability proposed models. Moreover, addresses imbalanced particularly IQ-OTHNCCD, a specific focus model resilience robustness. To assess overall performance, conducted experiments different scenarios. The D1 achieved an impressive 99.80% accuracy dataset, Jaccard Index (J) 0.8371, Matthew's Correlation Coefficient (MCC) 0.9073, Cohen's Kappa (Kp) 0.9057, Critical Success (CSI) 0.8213. When subjected 10-fold cross-validation LC25000, averaged (avg) 99.96% (avg J, MCC, Kp, CSI 0.9993, 0.9987, 0.9853, 0.9990), surpassing recent reported performances. Furthermore, ensemble D2 reached 93% (J, 0.7556, 0.8839, 0.8796, 0.7140) exceeding benchmarks aligning other results. Efficiency evaluations For instance, training only 10% resulted high rates 99.19% 0.9840, 0.9898, 0.9837) (D1) 99.30% 0.9863, 0.9913, 0.9861) (D2). In NCT-CRC-HE-100K, 99.53% 0.9906, 0.9946, 0.9906) 30% dataset testing remaining 70%. CRC-VAL-HE-7K, 95% 0.8845, 0.9455, 0.9452, 0.8745) 96% 0.8926, 0.9504, 0.9503, 0.8798), respectively, outperforming previously results closely others. Lastly, just significant outperformance InceptionV3, Xception, DenseNet201 benchmarks, achieving rate 82.98% 0.7227, 0.8095, 0.8081, 0.6671). Finally, using explainable AI algorithms such as Grad-CAM, Grad-CAM++, Score-CAM, Faster along emphasized versions, we visualized features last layer well CT-scan samples. models, multi-modality, robustness, efficiency classification, hold promise advancements medical They have potential revolutionize improve healthcare accessibility worldwide.

Language: Английский

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A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 252, P. 124114 - 124114

Published: May 20, 2024

Language: Английский

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Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection

Technologies, Journal Year: 2025, Volume and Issue: 13(2), P. 54 - 54

Published: Feb. 1, 2025

The automated and precise classification of lung colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, ineffectiveness utilising multiscale features. To this end, the present research introduces CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction selection overcome aforementioned constraints. Initially, it extracts attributes two separate layers (pooling fully connected) three pre-trained CNNs (MobileNet, ResNet-18, EfficientNetB0). Second, uses benefits canonical correlation analysis for dimensionality reduction pooling layer reduce complexity. In addition, features encapsulate both high- low-level representations. Finally, benefit multiple network architectures while reducing proposed merges dual variables then applies variance (ANOVA) Chi-Squared most discriminative integrated CNN architectures. is assessed LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, k-nearest neighbours. experimental results exhibited outstanding performance, attaining 99.8% accuracy cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding performance individual markedly diminishing framework’s capacity sustain exceptional limited set renders especially advantageous clinical applications where diagnostic precision efficiency critical. These findings confirm efficacy multi-CNN, multi-layer methodology enhancing mitigating constraints systems.

Language: Английский

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Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: April 19, 2024

Abstract The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap medical imaging and diagnostics. precision these CNN-based classifiers detecting analyzing symptoms has opened new avenues early detection treatment planning. However, despite technological strides, there are critical areas that require further exploration development. In this landscape, computer-aided diagnostic systems artificial intelligence, particularly deep learning methods like region proposal network, dual path local binary patterns, become pivotal. face challenges such as limited interpretability, data variability handling issues, insufficient generalization. Addressing is key to enhancing accurate diagnosis, fundamental for effective planning improving patient outcomes. This study introduces an advanced approach combines Network with DenseNet, leveraging fusion mobile edge computing identification classification. integration techniques enables system amalgamate information from multiple sources, robustness accuracy model. Mobile facilitates faster processing analysis CT scan images by bringing computational resources closer source, crucial real-time applications. undergo preprocessing, including resizing rescaling, optimize feature extraction. DenseNet-CNN model, strengthened capabilities, excels extracting features scans, effectively distinguishing between healthy cancerous tissues. classification categories include Normal, Benign, Malignant, latter sub-categorized into adenocarcinoma, squamous cell carcinoma, large carcinoma. controlled experiments, outperformed existing state-of-the-art methods, achieving impressive 99%. indicates its potential powerful tool cancer, advancement technology.

Language: Английский

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Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

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

Published: Dec. 4, 2024

Language: Английский

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Deep Learning Algorithms for Colon Cancer Detection: A Comparative Study with Traditional Machine Learning Methods

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 116 - 129

Published: Jan. 1, 2025

Language: Английский

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

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

Published: Jan. 1, 2025

Language: Английский

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Fused multi-level attention features with a constraint fusion network for colorectal tissue classification using histopathological images

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 124, P. 110296 - 110296

Published: April 6, 2025

Language: Английский

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Transfer Learning Approach with ResNet Architecture for Colon Cancer Classification Using Histopathological Images

Studies in big data, Journal Year: 2025, Volume and Issue: unknown, P. 143 - 153

Published: Jan. 1, 2025

Language: Английский

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ELW‐CNN: An extremely lightweight convolutional neural network for enhancing interoperability in colon and lung cancer identification using explainable AI

Healthcare Technology Letters, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Abstract Cancer is a condition in which cells the body grow uncontrollably, often forming tumours and potentially spreading to various areas of body. hazardous medical case history analysis. Every year, many people die cancer at an early stage. Therefore, it necessary accurately identify effectively treat save human lives. However, machine deep learning models are effective for identification. effectiveness these efforts limited by small dataset size, poor data quality, interclass changes between lung squamous cell carcinoma adenocarcinoma, difficulties with mobile device deployment, lack image individual‐level accuracy tests. To overcome difficulties, this study proposed extremely lightweight model using convolutional neural network that achieved 98.16% large colon individually 99.02% 99.40% cancer. The used only 70 thousand parameters, highly real‐time solutions. Explainability methods such as Grad‐CAM symmetric explanation highlight specific regions input affect decision model, helping potential challenges. will aid professionals developing automated accurate approach detecting types

Language: Английский

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