Accurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on...

Colorectal cancer (CRC) is one of the most common and deadly forms of cancer worldwide, necessitating accurate and early detection to improve treatment outcomes. Traditional diagnostic methods often rely on manual examination of pathological images, which can be time-consuming and prone to human error. This study presents an advanced approach for colorectal cancer detection using a Random Hinge Exponential Distribution coupled Attention Network (RHED-CANet) on pathological images. The input dataset is sourced from the TCGA-CRC-DX cohort and the CRC dataset, both widely recognized for their comprehensive coverage of colorectal cancer cases. Pre-processing and feature extraction are performed using a Modified Square Root Sage-Husa Adaptive Kalman Filter combined with a Spike-Driven Transformer, enhancing noise reduction and feature clarity. Segmentation is achieved through an EfficientNetV2L Inception Transformer, ensuring precise delineation of cancerous regions. The final classification utilizes the RHED-CANet, a network tailored to handle the complexities of pathological data with high accuracy. This methodology achieved remarkable results, with an accuracy of 99.9% and a precision of 99.7%. These performance metrics underscore the method's ability to minimize false positives and enhance diagnostic accuracy. The proposed approach offers significant advantages, including a reduction in diagnostic time and a substantial improvement in detection accuracy, making it a promising tool for clinical applications. Despite its excellent accuracy, the suggested RHED-CANet technique has drawbacks, such as overfitting the TCGA-CRC-DX and CRC datasets by reducing generalizability on other datasets comprising other cancer types or image qualities. The actual application of the techniques in real-time clinical applications may be hampered by this computational load, especially in settings with limited resources, and the model's potential computational complexity due to multiple advanced processing steps. Additionally, the efficiency of training may be impacted by biased inputs, particularly for minor CRC subtypes.