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Artificial Neural Network based Detection of Renal Tumors using CT Scan Image Processing

机译:基于人工神经网络的CT扫描图像处理对肾脏肿瘤的检测

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Renal tumour segmentation and analysis is a very important step for doctors in deciding the stage of cancer and determining the method of treatment. This paper examines a novel approach to develop an efficient algorithm to detect and further analyse the renal cancer tumours. The algorithm has been employed to pre-process and segment the image for better visualization and segmentation of the visible tumour. The pre-processing involves hybrid filter for noise removal and image enhancement. An artificial neural network has also been used by means of Hybrid Self Organizing Maps using which we have used for clustering of the image data and thereby highlighting the detected region. The correct output obtained by the medical team is then compared with the resultant image in order to improve algorithm to aptly understand the affected regions in human body and aid in better visualization of the tumor. We then apply a region growing method which looks for similar intensity regions in the images and thus segment outs the tumour from the processed image.
机译:肾脏肿瘤的分割和分析对于医生确定癌症的阶段和确定治疗方法是非常重要的一步。本文探讨了一种开发有效算法以检测和进一步分析肾癌肿瘤的新颖方法。该算法已被用于预处理和分割图像,以更好地可视化和分割可见肿瘤。预处理涉及混合滤波器,用于去除噪声和增强图像。人工神经网络也已经通过混合自组织图被使用,我们已经使用它来对图像数据进行聚类,从而突出显示了检测到的区域。然后,将医疗团队获得的正确输出与结果图像进行比较,以改进算法以恰当地理解人体受影响区域并帮助更好地可视化肿瘤。然后,我们应用区域增长方法,该方法在图像中寻找相似的强度区域,从而从处理后的图像中分割出肿瘤。

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