Vinoth N A S, Kalaivani J, Arieth R Madonna, Sivasakthiselvan S, Park Gi-Cheon, Joshi Gyanendra Prasad, Cho Woong
Department of Computing Technologies, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India.
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India.
Sci Rep. 2025 Jul 7;15(1):24247. doi: 10.1038/s41598-025-10246-0.
Lung and colon cancers (LCC) are among the foremost reasons for human death and disease. Early analysis of this disorder contains various tests, namely ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT). Despite analytical imaging, histopathology is one of the effective methods that delivers cell-level imaging of tissue under inspection. These are mainly due to a restricted number of patients receiving final analysis and early healing. Furthermore, there are probabilities of inter-observer faults. Clinical informatics is an interdisciplinary field that integrates healthcare, information technology, and data analytics to improve patient care, clinical decision-making, and medical research. Recently, deep learning (DL) proved to be effective in the medical sector, and cancer diagnosis can be made automatically by utilizing the capabilities of artificial intelligence (AI), enabling faster analysis of more cases cost-effectively. On the other hand, with extensive technical developments, DL has arisen as an effective device in medical settings, mainly in medical imaging. This study presents an Enhanced Fusion of Transfer Learning Models and Optimization-Based Clinical Biomedical Imaging for Accurate Lung and Colon Cancer Diagnosis (FTLMO-BILCCD) model. The main objective of the FTLMO-BILCCD technique is to develop an efficient method for LCC detection using clinical biomedical imaging. Initially, the image pre-processing stage applies the median filter (MF) model to eliminate the unwanted noise from the input image data. Furthermore, fusion models such as CapsNet, EffcientNetV2, and MobileNet-V3 Large are employed for the feature extraction. The FTLMO-BILCCD technique implements a hybrid of temporal pattern attention and bidirectional gated recurrent unit (TPA-BiGRU) for classification. Finally, the beluga whale optimization (BWO) technique alters the hyperparameter range of the TPA-BiGRU model optimally and results in greater classification performance. The FTLMO-BILCCD approach is experimented with under the LCC-HI dataset. The performance validation of the FTLMO-BILCCD approach portrayed a superior accuracy value of 99.16% over existing models.
肺癌和结肠癌是导致人类死亡和患病的主要原因之一。对这种疾病的早期分析包括各种检查，即超声（US）、磁共振成像（MRI）和计算机断层扫描（CT）。尽管有分析成像技术，但组织病理学是提供被检查组织细胞水平成像的有效方法之一。这主要是因为接受最终分析和早期治疗的患者数量有限。此外，还存在观察者间误差的可能性。临床信息学是一个跨学科领域，它整合了医疗保健、信息技术和数据分析，以改善患者护理、临床决策和医学研究。最近，深度学习（DL）被证明在医疗领域是有效的，利用人工智能（AI）的能力可以自动进行癌症诊断，可以更经济高效地对更多病例进行更快分析。另一方面，随着技术的广泛发展，深度学习已成为医疗环境中的一种有效工具，但主要应用于医学成像领域。本研究提出了一种用于准确诊断肺癌和结肠癌的转移学习模型与基于优化的临床生物医学成像的增强融合（FTLMO-BILCCD）模型。FTLMO-BILCCD技术的主要目标是以临床生物医学成像为基础，开发一种有效的肺癌和结肠癌检测方法。首先，图像预处理阶段应用中值滤波（MF）模型去除输入图像数据中的不必要噪声。此外，还采用了诸如胶囊网络（CapsNet）、高效网络V2（EffcientNetV2）和移动网络V3大模型（MobileNet-V3 Large）等融合模型进行特征提取。FTLMO-BILCCD技术采用时间模式注意力和双向门控循环单元（TPA-BiGRU）混合模型进行分类。最后采用白鲸优化（BWO）技术对TPA-BiGRU模型的超参数范围进行优化，从而获得更高的分类性能。FTLMO-BILCCD方法在LCC-HI数据集上进行了实验。FTLMO-BILCCD方法的性能验证表明，其准确率高达99.16%，优于现有模型。
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