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Abstract：
Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.
摘要：
结肠癌是一种普遍且可能致命的疾病，需要早期和准确的诊断才能有效治疗。传统的结肠癌诊断方法往往在准确性和效率方面存在局限性。导致早期发现和治疗的挑战。为了应对这些挑战,本文介绍了一种利用人工智能的创新方法，特别是卷积神经网络(CNN)和FisherMantis优化器，用于自动检测结肠癌。深度学习技术的利用，特别是CNN,能够从医学成像数据中提取复杂的特征，提供了一个强大而有效的诊断模型。此外,FisherMantis优化器,一种生物启发的优化算法，其灵感来自于羚羊虾的狩猎行为，用于微调CNN的参数，提高其收敛速度和性能。这种混合方法旨在通过利用深度学习和自然优化的优势来解决传统诊断方法的局限性，以提高结肠癌诊断的准确性和有效性。在包含结肠癌图像的综合数据集上对所提出的方法进行了评估，结果证明了其优于传统诊断方法。CNN-FisherMantis优化器模型表现出高灵敏度，特异性，以及区分癌症和非癌结肠组织的总体准确性。生物优化算法与深度学习技术的集成不仅有助于结肠癌计算机辅助诊断工具的进步，而且有望增强对这种疾病的早期检测和诊断。从而促进及时干预和改善患者预后。各种CNN设计，如GoogLeNet和ResNet-50，用于捕获与结肠疾病相关的特征。然而,由于特征丰富，在特征提取和数据分类中都引入了不准确性。为了解决这个问题，使用FisherMantisOptimizer算法实现了特征减少技术，优于遗传算法和模拟退火等替代方法。在对不同指标的评估中获得了令人鼓舞的结果，包括灵敏度,特异性，准确度,和F1-Score,被发现是94.87%，96.19%,97.65%,96.76%，分别。
