Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques

Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatment quality and the survival rate. This paper presents a comprehensive approach to enhance colon cancer detection and classification. The histopathological images are gathered from the CRC-VAL-HE-7K dataset. The images undergo preprocessing to improve quality, followed by augmentation to increase dataset size and enhance model generalization. A deep learning based transformer model is designed for efficient feature extraction and enhancing classification by incorporating a convolutional neural network (CNN). A cross-transformation model captures long-range dependencies between regions, and an attention mechanism assigns weights to highlight crucial features. To boost classification accuracy, a Siamese network distinguishes colon cancer tissue classes based on probabilities. Optimization algorithms fine-tune model parameters, categorizing colon cancer tissues into different classes. The multi-class classification performance is evaluated in the experimental evaluation, which demonstrates that the proposed model provided highest accuracy rate of 98.84%. In this research article, the proposed method achieved better performance in all analyses by comparing with other existing methods.Research Highlights Deep learning-based techniques are proposed. DL methods are used to enhance colon cancer detection and classification. CRC-VAL-HE-7K dataset is utilized to enhance image quality. Hybrid particle swarm optimization (PSO) and dwarf mongoose optimization (DMO) are used. The deep learning models are tuned by implementing the PSO-DMO algorithm. Advanced techniques: explore the integration of cross transformers, attention mechanisms, and Siamese networks to enhance feature extraction capabilities, enabling the model to capture intricate patterns within histopathological images. Improved classification: by leveraging these advanced deep learning techniques, the improvement is provided to accuracy and colon cancer tissue classification that ultimately benefits both patients and healthcare professionals. Reduced interobserver variability: the proposed research endeavors to reduce the subjectivity associated with manual diagnosis by providing an automated and consistent approach to colon cancer diagnosis. Benchmarking: the quantitative analyses are conducted with diverse performance evaluation measures for evaluating performance of the proposed model and benchmarks are used to assess the effectiveness of the proposed model in clinical settings. Evaluation outcome: multiple classes are categorized from the CRC-VAL-HE-7K dataset by fine-tuning the parameters of the deep learning model.image