This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification. Various image processing techniques, including Gaussian smoothing, bilateral filtering, and K-means clustering, are employed to preprocess the input images and highlight tumor regions. The CNN model is trained and evaluated on a dataset of medical images, with augmentation and data generators utilized to enhance model generalization. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images, paving the way for improved diagnostic tools in healthcare.

翻译：本研究提出一种基于卷积神经网络（CNN）的机器学习方法，用于医学图像中的肿瘤检测。研究重点在于通过预处理技术增强与肿瘤检测相关的图像特征，随后开发并训练CNN模型以实现精确分类。采用高斯平滑、双边滤波及K-means聚类等多种图像处理技术对输入图像进行预处理，突出肿瘤区域。CNN模型在医学图像数据集上进行训练与评估，并通过数据增强与生成器提升模型泛化能力。实验结果证明了所提方法在医学图像中准确检测肿瘤的有效性，为改进医疗诊断工具奠定了基础。