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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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 Hybrid Deep Learning and Machine Learning Approach with Mobile-EfficientNet and Grey Wolf Optimizer for Lung and Colon Cancer Histopathology Classification

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

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Improved Water Strider Algorithm With Convolutional Autoencoder for Lung and Colon Cancer Detection on Histopathological Images

IEEE Access, Journal Year: 2023, Volume and Issue: 12, P. 949 - 956

Published: Dec. 25, 2023

Lung and colon cancers are deadly diseases that can develop concurrently in organs undesirably affect human life some special cases. The detection of these from histopathological images poses a complex challenge medical diagnostics. Advanced image processing techniques, including deep learning algorithms, offer solution by analyzing intricate patterns structures slides. integration artificial intelligence analysis not only improves the proficiency cancer but also holds potential to increase prognostic assessments, eventually contributing effective treatment strategies for patients with lung cancers. This manuscript presents an Improved Water Strider Algorithm Convolutional Autoencoder Colon Cancer Detection (IWSACAE-LCCD) on HIs. major aim IWSACAE-LCCD technique aims detect cancer. For noise removal process, median filtering (MF) approach be used. Besides, convolutional neural network based MobileNetv2 model applied as feature extractor IWSA hyperparameter optimizer. Finally, autoencoder (CAE) presence To enhance results technique, series simulations were performed. obtained highlighted outperforms other approaches terms different measures.

Language: Английский

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Editorial for Special Issue “Image Analysis and Machine Learning in Cancers”

Cancers, Journal Year: 2025, Volume and Issue: 17(5), P. 778 - 778

Published: Feb. 25, 2025

Cancer detection has been a great challenge in many fields of science [...].

Language: Английский

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Multi head attention based conditional progressive GAN for colon cancer histopathological images analysis

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Language: Английский

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A robust transfer learning approach with histopathological images for lung and colon cancer detection using EfficientNetB3

Healthcare Analytics, Journal Year: 2025, Volume and Issue: unknown, P. 100391 - 100391

Published: April 1, 2025

Language: Английский

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Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model

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

Published: Nov. 11, 2024

Cancer is a life-threatening disease resulting from genetic disorder and range of metabolic anomalies. In particular, lung colon cancer (LCC) are among the major causes death in humans. The histopathological diagnoses critical detecting this kind cancer. This diagnostic testing substantial part patient's treatment. Thus, recognition classification LCC cutting-edge research regions, particularly biological healthcare medical fields. Earlier diagnosis can significantly reduce risk fatality. Machine learning (ML) deep (DL) models used to hasten these analyses, allowing researcher workers analyze considerable proportion patients limited time at low price. manuscript proposes Predictive Analytics Complex Healthcare Systems Using DL-based Disease Diagnosis Model (PACHS-DLBDDM) method. proposed PACHS-DLBDDM method majorly concentrates on detection LCC. At primary stage, methodology utilizes Gabor Filtering (GF) preprocess input imageries. Next, employs Faster SqueezeNet generate feature vectors. addition, convolutional neural network with long short-term memory (CNN-LSTM) approach classify To optimize hyperparameter values CNN-LSTM approach, Chaotic Tunicate Swarm Algorithm (CTSA) was implemented improve accuracy classifier results. simulation examined image dataset. performance validation model portrays superior value 99.54% over other DL models.

Language: Английский

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Identification of Anomalies in Lung and Colon Cancer Using Computer Vision-Based Swin Transformer with Ensemble Model on Histopathological Images

Bioengineering, Journal Year: 2024, Volume and Issue: 11(10), P. 978 - 978

Published: Sept. 28, 2024

Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention precise diagnosis for efficient treatment. The conventional diagnostic techniques LCC regularly encounter constraints in terms of efficiency accuracy, thus causing challenges primary recognition Early the can immensely reduce probability death. In medical practice, histopathological study tissue samples generally uses classical model. Still, automated devices exploit artificial intelligence (AI) produce results diagnosis. histopathology, both machine learning (ML) deep (DL) approaches be deployed owing to their latent ability analyzing predicting physically accurate molecular phenotypes microsatellite uncertainty. this background, presents novel technique called Colon Cancer using Swin Transformer with an Ensemble Model on Histopathological Images (LCCST-EMHI). proposed LCCST-EMHI method focuses designing DL model classification images (HI). order achieve this, utilizes bilateral filtering (BF) get rid noise. Further, (ST) also employed purpose feature extraction. For detection process, ensemble classifier used three techniques: bidirectional long short-term memory multi-head (BiLSTM-MHA), Double Deep Q-Network (DDQN), sparse stacked autoencoder (SSAE). Eventually, hyperparameter selection models implemented utilizing walrus optimization algorithm (WaOA) method. illustrate promising performance approach, extensive range simulation analyses was conducted benchmark dataset. experimentation demonstrated approach over other recent methods.

Language: Английский

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Advancements and Challenges in the Image-Based Diagnosis of Lung and Colon Cancer: A Comprehensive Review

Cancer Informatics, Journal Year: 2024, Volume and Issue: 23

Published: Jan. 1, 2024

Image-based diagnosis has become a crucial tool in the identification and management of various cancers, particularly lung colon cancer. This review delves into latest advancements ongoing challenges field, with focus on deep learning, machine image processing techniques applied to X-rays, CT scans, histopathological images. Significant progress been made imaging technologies like computed tomography (CT), magnetic resonance (MRI), positron emission (PET), which, when combined learning artificial intelligence (AI) methodologies, have greatly enhanced accuracy cancer detection characterization. These advances enabled early detection, more precise tumor localization, personalized treatment plans, overall improved patient outcomes. However, despite these improvements, persist. Variability interpretation, lack standardized diagnostic protocols, unequal access advanced technologies, concerns over data privacy security within AI-based systems remain major obstacles. Furthermore, integrating broader clinical information is achieving comprehensive approach treatment. provides valuable insights recent developments image-based for underscoring both remarkable hurdles that still need be overcome optimize care.

Language: Английский

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