Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model’s performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.
食管癌因其早期阶段缺乏明显特征，成为癌症相关死亡的主要原因之一。多项研究表明，窄带成像技术在检测食管癌方面比白光成像具有更高的准确性、敏感性和特异性。因此，本研究创新性地采用与装饰相关的色彩空间将白光图像转换为窄带成像图像，为提升早期食管癌的检测能力提供了新方法。本研究共使用3415张白光图像及对应的3415张模拟窄带成像图像进行分析，结合YOLOv5算法分别对白光图像和窄带成像图像进行训练，展示了先进目标检测技术在医学图像分析中的适应性。模型性能评估基于生成的混淆矩阵及五个关键指标：训练模型的精确率、召回率、特异性、准确率和F1分数。该模型经过训练可准确识别食管癌的三种特定表现，即异型增生、鳞状细胞癌和息肉，实现了精细化和针对性分析，从多角度解析食管癌病理特征以获得更全面的认知。在检测癌前病变异型增生方面，窄带成像模型有效提升了召回率和准确率，这可能有助于提高整体五年生存率。相反，在鳞状细胞癌类别中准确率和召回率有所下降，而窄带成像与白光成像模型在息肉识别方面表现相近。窄带成像模型在异型增生、鳞状细胞癌和息肉类别的准确率分别为0.60、0.81和0.66，同类别召回率分别为0.40、0.73和0.76。白光成像模型在相应类别的准确率分别为0.56、0.99和0.65，召回率分别为0.39、0.86和0.78。训练图像数量有限是窄带成像模型性能未达最优的主要原因，通过增加数据集可改善此状况。
肿瘤(癌症)患者之家