Colorectal cancer (CRC) is a significant global health concern, ranking as the second most common cancer among females and the third among males, with an increasing number of cases each year. Early and accurate diagnosis is crucial for effective treatment, particularly in personalized medicine, where pathology diagnoses complemented by predictive and prognostic biomarker information play a vital role.
Limitations of Conventional Histopathological Analysis
Histopathological image (HI) analysis is the standard method used by pathologists to classify colorectal cancer. However, this approach is often subjective, leading to potential diagnostic inconsistencies and errors. Additionally, the increasing workload in pathology laboratories, along with intra- and inter-variability in biomarker assessments, highlights the need for reliable, machine-based diagnostic techniques to enhance accuracy and efficiency in routine practice.
AI-Powered Diagnostic Framework: CCD-ODFFBI
Artificial intelligence (AI) has revolutionized the healthcare industry, achieving remarkable success in various applications. In recent years, computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly due to advancements in machine learning (ML) and deep learning (DL) models. In this context, a novel method called Colorectal Cancer Diagnosis using the Optimal Deep Feature Fusion Approach on Biomedical Images (CCD-ODFFBI) has been introduced. The CCD-ODFFBI technique aims to improve colorectal cancer detection by utilizing a combination of deep learning models—MobileNet, SqueezeNet, and SE-ResNet—for feature extraction. The optimization of hyperparameters is performed using the Osprey optimization algorithm (OOA), ensuring the model’s efficiency. Finally, a deep belief network (DBN) is employed for accurate classification of CRC cases.
Performance Evaluation and Clinical Potential
To evaluate the effectiveness of the CCD-ODFFBI method, extensive simulations were conducted using the Warwick-QU dataset. The results demonstrated that this approach achieved a superior accuracy of 99.39%, significantly outperforming existing diagnostic techniques. The exceptional performance of the CCD-ODFFBI model underscores the potential of AI-driven diagnostic systems in enhancing colorectal cancer detection, ultimately improving patient outcomes and streamlining pathology workflows.
References : https://www.nature.com/articles/s41598-024-83466-5