Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model

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

Published: Sept. 3, 2024

Cancer seems to have a vast number of deaths due its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories cancer that may affect males females occur worldwide are colon lung cancer. A precise on-time analysis this can increase the survival rate improve appropriate treatment characteristics. An efficient effective method speedy accurate recognition tumours in areas is provided as an alternative methods. Earlier diagnosis disease on front drastically reduces chance death. Machine learning (ML) deep (DL) approaches accelerate diagnosis, facilitating researcher workers study majority patients limited period at low cost. This research presents Histopathological Imaging Early Detection Lung Colon via Ensemble DL (HIELCC-EDL) model. HIELCC-EDL technique utilizes histopathological images identify (LCC). To achieve this, uses Wiener filtering (WF) noise elimination. In addition, model channel attention Residual Network (CA-ResNet50) complex feature patterns. Moreover, hyperparameter selection CA-ResNet50 performed using tuna swarm optimization (TSO) technique. Finally, detection LCC achieved by ensemble three classifiers such extreme machine (ELM), competitive neural networks (CNNs), long short-term memory (LSTM). illustrate promising performance model, complete set experimentations was benchmark dataset. experimental validation portrayed superior accuracy value 99.60% over recent approaches.

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

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

Published: Jan. 8, 2025

Language: Английский

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Hybrid Feature Extraction and Transfer Learning Approach for Multi-Class Histopathological Image Classification in Colorectal Cancer

Published: Jan. 1, 2025

Language: Английский

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

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

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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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Involution-based efficient autoencoder for denoising histopathological images with enhanced hybrid feature extraction

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 192, P. 110174 - 110174

Published: April 24, 2025

Language: Английский

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Colon Cancer Detection Using Deep Learning:A Comprehensive Review

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 325 - 331

Published: April 15, 2025

Colon cancer (CRC) is a leading cause of cancer-related deaths globally, emphasizing the need for accurate and timely detection methods. In this study, we apply deep learning techniques, specifically transfer with VGG16, MobileNet, ResNet architectures, to classify from histopathological images. By leveraging pre-trained models, aim improve accuracy reduce computational complexity, facilitating early diagnosis in clinical settings. The dataset, sourced Kaggle, comprises diverse collection images representing both benign malignant tissues. Each model was fine-tuned on dataset after applying pre-processing techniques standardize enhance image quality. performance evaluated using metrics such as accuracy, sensitivity, specificity, F1 score, demonstrating effectiveness detection. Our results show that particularly ResNet, achieve high detecting cancer, offering promising solution improving diagnostic practices. integration these models into healthcare systems has potential accelerate detection, errors, patient outcomes

Language: Английский

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Real-time Tumor Detection Using Electromagnetic Signals With Memristive Echo State Networks

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(20), P. 33712 - 33721

Published: July 22, 2024

Early detection and diagnosis of brain tumors are great significance, as they can be life saving. Current state-of-the-art methods, including X-ray magnetic resonance imaging (MRI) require more resources advanced medical facilities, cannot used for continuous or long-term monitoring. The importance this contribution lies in the timely these conditions. In our work, we propose a method identifying that overcomes shortcomings. Two antennas, Ant1 Ant2 were around head, changes transmission coefficients (S21) monitored. Experiments conducted on human head-shaped container, data obtained transferred to memristor crossbar array using Voltage Threshold Adaptive Memristor (VTEAM) model prediction cancer. proposed is implementing echo state networks detects presence cancer with an accuracy 77.5% after incorporating compensation signal integrity influences.

Language: Английский

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A Review on Lung and Colon Combine Cancer Detection using ML and DL Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology, Journal Year: 2024, Volume and Issue: 10(5), P. 24 - 35

Published: Sept. 5, 2024

The detection of lung and colon cancer is a critical challenge in medical diagnosis, machine learning (ML) deep (DL) techniques are increasingly being used to enhance accuracy efficiency. This review focuses on the integration ML DL methods for combined cancer, emphasizing their strengths, limitations, future potential. motivation behind this study address growing demand accurate early these cancers, which significantly impacts treatment outcomes. Current often struggle with feature complexity, image variability, computational intensity, limit real-world applicability. aim consolidate various that have been employed purpose, highlighting how hybrid models can improve rates. objective provide comprehensive analysis different methodologies, datasets, pre-processing techniques, extraction methods, evaluation parameters. also explores recent advancements, such as transfer fine-tuning further optimize performance detection. findings suggest while current show promise, improvements model generalization, interpretability, efficiency required overcome existing limitations expand clinical use.

Language: Английский

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